project research methods and statistics

Research Methods: A Student's Comprehensive Guide: Fundamentals

• Research Approaches
• Types of Sources
• Accessing Resources
• Evaluating Sources
• Question Crafting
• Search Strategies
• Annotated Bibliography
• Literature Reviews
• Citations This link opens in a new window

Research Methods: Essential Foundations

Key research methods, method match, qualitative methods.

• Focus:  Understanding the nuances and depth of non-numerical data, offering rich, detailed insights into your research topic. 
• Common Techniques:  Interviews, focus groups, case studies, and content analysis.
• Application:  Best for exploring new ideas, developing theories, and understanding individual or group experiences in detail.

Quantitative Methods

• Focus:  Utilizes numerical data and statistical techniques to test hypotheses and draw generalizable conclusions. 
• Common Techniques:  Encompasses surveys, experiments, and statistical analysis. 
• Application:  Suitable for measuring variables, validating theories, and making data-driven predictions.

Mixed Methods

• Focus:  Combining qualitative and quantitative data to gain a fuller understanding of a research question. 
• Common Techniques:  Sequential or concurrent use of interviews, surveys, and experiments.
• Application:  Ideal when research requires both detailed insights and measurable data to draw well-rounded conclusions.

Choosing the right research method is crucial for project success. Your choice should align with your research question, data needs, and study goals. Here’s how to select the best method:

Qualitative Research : Ideal for exploring complex ideas and answering  “How?”  or  “Why?”  questions. This approach provides rich, detailed insights through techniques such as interviews and focus groups. It’s great for understanding experiences and developing theories.

Quantitative Research : Best for measuring variables and analyzing numerical data to address  “How much?”  or  “What impact?”  questions. This method involves surveys and statistical analysis, making it suitable for testing hypotheses and validating theories.

Mixed Methods : Combining qualitative and quantitative approaches offers a comprehensive analysis. Use this method to explore the  “why”  behind data or to support qualitative findings with numerical evidence, giving you a fuller perspective on your research question.

Considerations

• Research Focus:  Determine what you aim to uncover or prove.
• Data:  Decide if you need detailed narratives or numerical data. 
• Resources:  Evaluate your available time, tools, and data  access.
• Purpose:  Consider whether you are exploring concepts or testing theories.
• Student Attitudes Toward Remote Learning : Qualitative methods like interviews provide in-depth personal insights.
• Impact of Exercise on Academic Performance : Quantitative methods such as surveys are effective for statistical analysis and identifying trends.
• When in doubt, start with your research question.  A well-defined question will naturally guide you to the most suitable method, whether it’s qualitative, quantitative, or mixed.

Ethical Principles

Informed consent, confidentiality, avoiding bias.

• Ethical Review Boards
• Informed Consent:  Clearly explain the study's purpose, methods, risks, and benefits to participates. Obtained documented consent, either through written forms or verbal agreements (recorded with permission).
• Autonomy:  Ensure participates can make their own informed decisions about participating and have the freedom to withdraw at any time without facing consequences.
• Minimizing Harm:  Design studies to minimize risks and harm. Evaluate potential risks carefully and implement strategies to reduce them.
• Maximizing Benefits:  Ensure the research provides value and contributes positively to the field of society, with the benefits outweighing any risks.
• Fair Selection:  Distribute the benefits and burdens of research equitably. Avoid exploiting vulnerable populations or unfairly burdening any group.
• Equitable Access:  Ensure all participants have equal access to the benefits of the research and avoid discriminatory practices.

What is Informed Consent?  Informed Consent is all about making sure participants know exactly what they're getting into before they agree to take part in research. It's about honesty, clarity, and respect. 

Key Elements:

• Clear Explanation:  Participants should understand the purpose of the study, what they'll be asked to do, any possible risks, and the potential benefits. This means explaining everything in simple, straightforward language—no confusing jargon. 
• Voluntary Participation:  Participation must be completely voluntary. That means no pressure to join, and participants can change their minds and leave the study anytime without any consequences.
• Understanding:  It's crucial that participants truly understand what they're consenting to. They should feel free to ask questions and get clear answers before agreeing to take part.
• Written Agreement:  Typically, participants will sign a consent form that summarizes the study's details. This form is a record of their agreement to participate. 

Significance:  Informed consent isn't just a formality—it's about respecting the rights and dignity of those involved in your research. It ensures that everyone is on the same page and that participants feel valued and safe. 

For Special Cases:  When working with children, non-English speakers, or people with cognitive impairments, extra steps should be taken to ensure they understand and agree to participate. This might include using simpler language, translators, or getting permission from a guardian.

What is Confidentiality?  Confidentiality in research is the practice of protecting the private information of participants. It's about ensuring that any personal details shared during the study are kept secure and are not disclosed without permission.

• Anonymity v Confidentiality:  Anonymity means that even the researcher doesn't know who the participants are, while confidentiality means that the researcher knows but keeps that information private. Both are important, but confidentiality often allows for deeper, more personalized data collection while still protecting participants' privacy.
• Data Protection:  All personal data, such as names, addresses, and any identifying information, should be stored securely. This could mean using encrypted digital storage or keeping physical records in a locked, safe space. The goal is to prevent unauthorized access to this sensitive information.
• Limited Access:  Only the research team and necessary personnel should have access to confidential information. It's also essential to clarify with participants who will see their data and in what form. 
• De-Identification:  When presenting or publishing research findings, it's crucial to remove any information that could identify individual participants. This process, known as de-identification, ensures that the data shared publicly cannot be traced back to the participants.  
• Informed Consent:   Participants should be informed upfront about how their data will be used and who will have access to it. They should also be reassured that their privacy will be protected throughout the study.

Significance:   Maintaining confidentiality builds trust between researchers and participants. It encourages honest and open communication, which is vital for collecting accurate data. Participants are more likely to share sensitive information if they know  their privacy is safeguarded.

Handling Breaches:  In the  rare case of confidentiality breach, it's important to have a plan in place to address it. This includes promptly notifying participants, taking corrective measures, and ensuring such incidents don't happen again. 

What is Bias in Research?  Bias in research refers to any influence that unfairly skews the results of a study. It can occur at any stage of the research process, from planning and data collection to analysis and interpretation. Bias can lead to incorrect conclusions, reducing the validity and reliability of your research.

Types of Bias:

• For example, if a study only surveys college students about work-life balance, it might not accurately reflect the experience of those in different life stages. 
• Confirmation Bias:  This happens when researchers focus on data that supports their hypothesis while ignoring or downplaying evidence that contradicts it. This can lead to a one-sided interpretation of the results. 
• For instance, using a survey with leading questions can influence participants to respond in a certain way, distorting the findings.
• Rooting Bias:  Occurs when only certain results are reported, often because they are more favorable or expected. This can mislead readers and hide the full scope of the research findings.

How to Avoid Bias:

• Use Random Sampling:  Ensure that every individual in the population has an equal chance of being selected for the study. Random sampling helps create a more representative sample and reduces selection bias.
• Blind or Double-Blind Studies:  In a blind study, participants don't know which group they're in (e.g., treatment v control), while in a double-blind study, neither participants nor the researchers know. This method helps prevent both participant and researcher bias.
• For example, using the same questionnaire and instructions for all everyone guarantees uniform conditions. 
• Peer Review & Replication:  Have experts review your study to catch potential biases you might have missed. Encourage others to replicate your study to verify  accuracy and rule out bias. 
• Promote Transparency:  Clearly outline your methods, limitations, and any potential conflicts of interest. Acknowledging where bias might have crept in, even unintentionally, demonstrates integrity and allows others to account for it when interpreting your findings. 

Significance:  Avoiding bias is crucial for maintaining the credibility and reliability of your research. Unbiased research provides a more accurate representation of reality, leading to conclusions that can be trusted and built upon by others. It also helps ensure that your findings contribute positively to the broader field of study, rather than perpetuating misinformation.

Pro-Tip:  Always question your assumptions. Regularly re-evaluate your methods, seek feedback from peers, and be prepared to adjust your approach to minimize bias. This diligence will help you produce high-quality, trustworthy  research.

Ethical Review Board

Ethical Review Boards (ERBs)

Before starting a research project involving human participants, it's crucial to go through an ERB process. ERBs are panels of experts who assess the ethical aspects of your research plan to safeguard participants' rights and well-being. 

What Do ERBs Do?

ERBs review your research proposal to verify that it aligns with ethical standards. They focus on aspects like informed consent, risk minimization, and confidentiality. The board ensures that your study is designed to treat participants fairly, without exposing them to necessary harm. 

Key Consideration:

Submitting your research to an ERB isn't just a formality; it's a vital step to maintaining the integrity of your work. An ERB's approval signifies that your research meets high ethical standards, which helps build trust in your findings and protects the people who contribute to your study. 

• Foundations

Learning Objectives

Research methods are fundamental to conducting thorough and credible research. They provide the framework for collecting and analyzing data systematically, helping you build a solid foundation for your findings.

• Systematic Approach:  Research methods offer a structured way to gather and interpret data, ensuring consistency and repeatability in your research process.
• Credibility:  By applying well-established methods, you enhance the reliability and validity of your findings, making your results more trustworthy.
• Problem-Solving:  These methods enable you to address complex questions and generate actionable insights based on your research. 
• Identify key characteristics of qualitative and quantitative research methods.
• Understand the relevance of selecting appropriate methods for specific research questions.
• Apply criteria to choose the right research approach based on research goals. 
• Next: Research Approaches >>
• Last Updated: Sep 20, 2024 3:27 PM
• URL: https://tsu.libguides.com/researchmethods

Effective Use of Statistics in Research – Methods and Tools for Data Analysis

Remember that impending feeling you get when you are asked to analyze your data! Now that you have all the required raw data, you need to statistically prove your hypothesis. Representing your numerical data as part of statistics in research will also help in breaking the stereotype of being a biology student who can’t do math.

Statistical methods are essential for scientific research. In fact, statistical methods dominate the scientific research as they include planning, designing, collecting data, analyzing, drawing meaningful interpretation and reporting of research findings. Furthermore, the results acquired from research project are meaningless raw data unless analyzed with statistical tools. Therefore, determining statistics in research is of utmost necessity to justify research findings. In this article, we will discuss how using statistical methods for biology could help draw meaningful conclusion to analyze biological studies.

Table of Contents

Role of Statistics in Biological Research

Statistics is a branch of science that deals with collection, organization and analysis of data from the sample to the whole population. Moreover, it aids in designing a study more meticulously and also give a logical reasoning in concluding the hypothesis. Furthermore, biology study focuses on study of living organisms and their complex living pathways, which are very dynamic and cannot be explained with logical reasoning. However, statistics is more complex a field of study that defines and explains study patterns based on the sample sizes used. To be precise, statistics provides a trend in the conducted study.

Biological researchers often disregard the use of statistics in their research planning, and mainly use statistical tools at the end of their experiment. Therefore, giving rise to a complicated set of results which are not easily analyzed from statistical tools in research. Statistics in research can help a researcher approach the study in a stepwise manner, wherein the statistical analysis in research follows –

1. Establishing a Sample Size

Usually, a biological experiment starts with choosing samples and selecting the right number of repetitive experiments. Statistics in research deals with basics in statistics that provides statistical randomness and law of using large samples. Statistics teaches how choosing a sample size from a random large pool of sample helps extrapolate statistical findings and reduce experimental bias and errors.

2. Testing of Hypothesis

When conducting a statistical study with large sample pool, biological researchers must make sure that a conclusion is statistically significant. To achieve this, a researcher must create a hypothesis before examining the distribution of data. Furthermore, statistics in research helps interpret the data clustered near the mean of distributed data or spread across the distribution. These trends help analyze the sample and signify the hypothesis.

3. Data Interpretation Through Analysis

When dealing with large data, statistics in research assist in data analysis. This helps researchers to draw an effective conclusion from their experiment and observations. Concluding the study manually or from visual observation may give erroneous results; therefore, thorough statistical analysis will take into consideration all the other statistical measures and variance in the sample to provide a detailed interpretation of the data. Therefore, researchers produce a detailed and important data to support the conclusion.

Types of Statistical Research Methods That Aid in Data Analysis

Statistical analysis is the process of analyzing samples of data into patterns or trends that help researchers anticipate situations and make appropriate research conclusions. Based on the type of data, statistical analyses are of the following type:

1. Descriptive Analysis

The descriptive statistical analysis allows organizing and summarizing the large data into graphs and tables . Descriptive analysis involves various processes such as tabulation, measure of central tendency, measure of dispersion or variance, skewness measurements etc.

2. Inferential Analysis

The inferential statistical analysis allows to extrapolate the data acquired from a small sample size to the complete population. This analysis helps draw conclusions and make decisions about the whole population on the basis of sample data. It is a highly recommended statistical method for research projects that work with smaller sample size and meaning to extrapolate conclusion for large population.

3. Predictive Analysis

Predictive analysis is used to make a prediction of future events. This analysis is approached by marketing companies, insurance organizations, online service providers, data-driven marketing, and financial corporations.

4. Prescriptive Analysis

Prescriptive analysis examines data to find out what can be done next. It is widely used in business analysis for finding out the best possible outcome for a situation. It is nearly related to descriptive and predictive analysis. However, prescriptive analysis deals with giving appropriate suggestions among the available preferences.

5. Exploratory Data Analysis

EDA is generally the first step of the data analysis process that is conducted before performing any other statistical analysis technique. It completely focuses on analyzing patterns in the data to recognize potential relationships. EDA is used to discover unknown associations within data, inspect missing data from collected data and obtain maximum insights.

6. Causal Analysis

Causal analysis assists in understanding and determining the reasons behind “why” things happen in a certain way, as they appear. This analysis helps identify root cause of failures or simply find the basic reason why something could happen. For example, causal analysis is used to understand what will happen to the provided variable if another variable changes.

7. Mechanistic Analysis

This is a least common type of statistical analysis. The mechanistic analysis is used in the process of big data analytics and biological science. It uses the concept of understanding individual changes in variables that cause changes in other variables correspondingly while excluding external influences.

Important Statistical Tools In Research

Researchers in the biological field find statistical analysis in research as the scariest aspect of completing research. However, statistical tools in research can help researchers understand what to do with data and how to interpret the results, making this process as easy as possible.

1. Statistical Package for Social Science (SPSS)

It is a widely used software package for human behavior research. SPSS can compile descriptive statistics, as well as graphical depictions of result. Moreover, it includes the option to create scripts that automate analysis or carry out more advanced statistical processing.

2. R Foundation for Statistical Computing

This software package is used among human behavior research and other fields. R is a powerful tool and has a steep learning curve. However, it requires a certain level of coding. Furthermore, it comes with an active community that is engaged in building and enhancing the software and the associated plugins.

3. MATLAB (The Mathworks)

It is an analytical platform and a programming language. Researchers and engineers use this software and create their own code and help answer their research question. While MatLab can be a difficult tool to use for novices, it offers flexibility in terms of what the researcher needs.

4. Microsoft Excel

Not the best solution for statistical analysis in research, but MS Excel offers wide variety of tools for data visualization and simple statistics. It is easy to generate summary and customizable graphs and figures. MS Excel is the most accessible option for those wanting to start with statistics.

5. Statistical Analysis Software (SAS)

It is a statistical platform used in business, healthcare, and human behavior research alike. It can carry out advanced analyzes and produce publication-worthy figures, tables and charts .

6. GraphPad Prism

It is a premium software that is primarily used among biology researchers. But, it offers a range of variety to be used in various other fields. Similar to SPSS, GraphPad gives scripting option to automate analyses to carry out complex statistical calculations.

This software offers basic as well as advanced statistical tools for data analysis. However, similar to GraphPad and SPSS, minitab needs command over coding and can offer automated analyses.

Use of Statistical Tools In Research and Data Analysis

Statistical tools manage the large data. Many biological studies use large data to analyze the trends and patterns in studies. Therefore, using statistical tools becomes essential, as they manage the large data sets, making data processing more convenient.

Following these steps will help biological researchers to showcase the statistics in research in detail, and develop accurate hypothesis and use correct tools for it.

There are a range of statistical tools in research which can help researchers manage their research data and improve the outcome of their research by better interpretation of data. You could use statistics in research by understanding the research question, knowledge of statistics and your personal experience in coding.

Have you faced challenges while using statistics in research? How did you manage it? Did you use any of the statistical tools to help you with your research data? Do write to us or comment below!

Frequently Asked Questions

Statistics in research can help a researcher approach the study in a stepwise manner: 1. Establishing a sample size 2. Testing of hypothesis 3. Data interpretation through analysis

Statistical methods are essential for scientific research. In fact, statistical methods dominate the scientific research as they include planning, designing, collecting data, analyzing, drawing meaningful interpretation and reporting of research findings. Furthermore, the results acquired from research project are meaningless raw data unless analyzed with statistical tools. Therefore, determining statistics in research is of utmost necessity to justify research findings.

Statistical tools in research can help researchers understand what to do with data and how to interpret the results, making this process as easy as possible. They can manage large data sets, making data processing more convenient. A great number of tools are available to carry out statistical analysis of data like SPSS, SAS (Statistical Analysis Software), and Minitab.

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• Methodology

Research Methods | Definition, Types, Examples

Research methods are specific procedures for collecting and analysing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs quantitative : Will your data take the form of words or numbers?
• Primary vs secondary : Will you collect original data yourself, or will you use data that have already been collected by someone else?
• Descriptive vs experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyse the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analysing data, examples of data analysis methods, frequently asked questions about methodology.

Data are the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach, where you use both qualitative and quantitative research methods.

Primary vs secondary data

Primary data are any original information that you collect for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary data are information that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data. But if you want to synthesise existing knowledge, analyse historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare them for analysis.

Data can often be analysed both quantitatively and qualitatively. For example, survey responses could be analysed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that were collected:

• From open-ended survey and interview questions, literature reviews, case studies, and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions.

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that were collected either:

• During an experiment.
• Using probability sampling methods .

Because the data are collected and analysed in a statistically valid way, the results of quantitative analysis can be easily standardised and shared among researchers.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Statistics for Research Students

(2 reviews)

Erich C Fein, Toowoomba, Australia

John Gilmour, Toowoomba, Australia

Tayna Machin, Toowoomba, Australia

Liam Hendry, Toowoomba, Australia

Copyright Year: 2022

ISBN 13: 9780645326109

Publisher: University of Southern Queensland

Language: English

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Reviewed by Sojib Bin Zaman, Assistant Professor, James Madison University on 3/18/24

From exploring data in Chapter One to learning advanced methodologies such as moderation and mediation in Chapter Seven, the reader is guided through the entire process of statistical methodology. With each chapter covering a different statistical... read more

Comprehensiveness rating: 5 see less

From exploring data in Chapter One to learning advanced methodologies such as moderation and mediation in Chapter Seven, the reader is guided through the entire process of statistical methodology. With each chapter covering a different statistical technique and methodology, students gain a comprehensive understanding of statistical research techniques.

Content Accuracy rating: 5

During my review of the textbook, I did not find any notable errors or omissions. In my opinion, the material was comprehensive, resulting in an enjoyable learning experience.

Relevance/Longevity rating: 5

A majority of the textbook's content is aligned with current trends, advancements, and enduring principles in the field of statistics. Several emerging methodologies and technologies are incorporated into this textbook to enhance students' statistical knowledge. It will be a valuable resource in the long run if students and researchers can properly utilize this textbook.

Clarity rating: 5

A clear explanation of complex statistical concepts such as moderation and mediation is provided in the writing style. Examples and problem sets are provided in the textbook in a comprehensive and well-explained manner.

Consistency rating: 5

Each chapter maintains consistent formatting and language, with resources organized consistently. Headings and subheadings worked well.

Modularity rating: 5

The textbook is well-structured, featuring cohesive chapters that flow smoothly from one to another. It is carefully crafted with a focus on defining terms clearly, facilitating understanding, and ensuring logical flow.

Organization/Structure/Flow rating: 5

From basic to advanced concepts, this book provides clarity of progression, logical arranging of sections and chapters, and effective headings and subheadings that guide readers. Further, the organization provides students with a lot of information on complex statistical methodologies.

Interface rating: 5

The available formats included PDFs, online access, and e-books. The e-book interface was particularly appealing to me, as it provided seamless navigation and viewing of content without compromising usability.

Grammatical Errors rating: 5

I found no significant errors in this document, and the overall quality of the writing was commendable. There was a high level of clarity and coherence in the text, which contributed to a positive reading experience.

Cultural Relevance rating: 5

The content of the book, as well as its accompanying examples, demonstrates a dedication to inclusivity by taking into account cultural diversity and a variety of perspectives. Furthermore, the material actively promotes cultural diversity, which enables readers to develop a deeper understanding of various cultural contexts and experiences.

In summary, this textbook provides a comprehensive resource tailored for advanced statistics courses, characterized by meticulous organization and practical supplementary materials. This book also provides valuable insights into the interpretation of computer output that enhance a greater understanding of each concept presented.

Reviewed by Zhuanzhuan Ma, Assistant Professor, University of Texas Rio Grande Valley on 3/7/24

The textbook covers all necessary areas and topics for students who want to conduct research in statistics. It includes foundational concepts, application methods, and advanced statistical techniques relevant to research methodologies. read more

The textbook covers all necessary areas and topics for students who want to conduct research in statistics. It includes foundational concepts, application methods, and advanced statistical techniques relevant to research methodologies.

The textbook presents statistical methods and data accurately, with up-to-date statistical practices and examples.

Relevance/Longevity rating: 4

The textbook's content is relevant to current research practices. The book includes contemporary examples and case studies that are currently prevalent in research communities. One small drawback is that the textbook did not include the example code for conduct data analysis.

The textbook break down complex statistical methods into understandable segments. All the concepts are clearly explained. Authors used diagrams, examples, and all kinds of explanations to facilitate learning for students with varying levels of background knowledge.

The terminology, framework, and presentation style (e.g. concepts, methodologies, and examples) seem consistent throughout the book.

The textbook is well organized that each chapter and section can be used independently without losing the context necessary for understanding. Also, the modular structure allows instructors and students to adapt the materials for different study plans.

The textbook is well-organized and progresses from basic concepts to more complex methods, making it easier for students to follow along. There is a logical flow of the content.

The digital format of the textbook has an interface that includes the design, layout, and navigational features. It is easier to use for readers.

The quality of writing is very high. The well-written texts help both instructors and students to follow the ideas clearly.

The textbook does not perpetuate stereotypes or biases and are inclusive in their examples, language, and perspectives.

Table of Contents

• Acknowledgement of Country
• Accessibility Information
• About the Authors
• Introduction
• I. Chapter One - Exploring Your Data
• II. Chapter Two - Test Statistics, p Values, Confidence Intervals and Effect Sizes
• III. Chapter Three- Comparing Two Group Means
• IV. Chapter Four - Comparing Associations Between Two Variables
• V. Chapter Five- Comparing Associations Between Multiple Variables
• VI. Chapter Six- Comparing Three or More Group Means
• VII. Chapter Seven- Moderation and Mediation Analyses
• VIII. Chapter Eight- Factor Analysis and Scale Reliability
• IX. Chapter Nine- Nonparametric Statistics

Ancillary Material

About the book.

This book aims to help you understand and navigate statistical concepts and the main types of statistical analyses essential for research students. 

About the Contributors

Dr Erich C. Fein  is an Associate Professor at the University of Southern Queensland. He received substantial training in research methods and statistics during his PhD program at Ohio State University.  He currently teaches four courses in research methods and statistics.  His research involves leadership, occupational health, and motivation, as well as issues related to research methods such as the following article: “ Safeguarding Access and Safeguarding Meaning as Strategies for Achieving Confidentiality .”  Click here to link to his  Google Scholar  profile.

Dr John Gilmour  is a Lecturer at the University of Southern Queensland and a Postdoctoral Research Fellow at the University of Queensland, His research focuses on the locational and temporal analyses of crime, and the evaluation of police training and procedures. John has worked across many different sectors including PTSD, social media, criminology, and medicine.

Dr Tanya Machin  is a Senior Lecturer and Associate Dean at the University of Southern Queensland. Her research focuses on social media and technology across the lifespan. Tanya has co-taught Honours research methods with Erich, and is also interested in ethics and qualitative research methods. Tanya has worked across many different sectors including primary schools, financial services, and mental health.

Dr Liam Hendry  is a Lecturer at the University of Southern Queensland. His research interests focus on long-term and short-term memory, measurement of human memory, attention, learning & diverse aspects of cognitive psychology.

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• The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences

The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences

Volume 1: building a program of research.

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The first of three volumes, the five sections of this book cover a variety of issues important in developing, designing, and analyzing data to produce high-quality research efforts and cultivate a productive research career. First, leading scholars from around the world provide a step-by-step guide to doing research in the social and behavioral sciences. After discussing some of the basics, the various authors next focus on the important building blocks of any study. In section three, various types of quantitative and qualitative research designs are discussed, and advice is provided regarding best practices of each. The volume then provides an introduction to a variety of important and cutting-edge statistical analyses. In the last section of the volume, nine chapters provide information related to what it takes to have a long and successful research career. Throughout the book, example and real-world research efforts from dozens of different disciplines are discussed.

‘This is an impressive volume with chapters written by well-qualified scholars on a wide variety of topics covering every step along the way of a research project, from theoretical foundations to study design to data collection to data analysis to career advice. I am glad to have this handbook at my fingertips.’

Brad J. Bushman - The Ohio State University, USA

‘This methods handbook wonderfully covers all aspects of research methods including topics that similar volumes often miss like theory development, working outside of academia, and how to be a successful scholar. It is a great read and wonderful addition to any scholar’s library.’

Bradley M. Okdie - The Ohio State University, USA

‘This book covers many topics in methodology and statistics from a wide variety of voices and perspectives. Collectively, they comprise what might be the most comprehensive single book about how to conduct social-behavioral research. Even more than covering the design and conduct of research, the book situates the reader in their role as a scholar, teacher, reviewer, and colleague in advancing rigorous and credible research.’

Brian Nosek - University of Virginia, USA

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The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences pp i-i

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Cambridge Handbooks in Psychology - Series page pp ii-ii

The cambridge handbook of research methods and statistics for the social and behavioral sciences - title page pp iii-iii, copyright page pp iv-iv, dedication pp v-vi, contents pp vii-ix, figures pp x-xiii, tables pp xiv-xv, contributors pp xvi-xviii, preface pp xix-xx, part i - from idea to reality: the basics of research pp 1-176, 1 - promises and pitfalls of theory pp 3-24.

• By Yzar S. Wehbe , Todd K. Shackelford , Laith Al-Shawaf

2 - Research Ethics for the Social and Behavioral Sciences pp 25-46

• By Ignacio Ferrero , Javier Pinto

3 - Getting Good Ideas and Making the Most of Them pp 47-64

• By Christian S. Crandall , Mark Schaller

4 - Literature Review pp 65-84

• By Rachel Adams Goertel

5 - Choosing a Research Design pp 85-102

• By Glynis M. Breakwell

6 - Building the Study pp 103-124

• By Martin Schnuerch , Edgar Erdfelder

7 - Analyzing Data pp 125-155

• By Roger Watt , Elizabeth Collins

8 - Writing the Paper pp 156-176

• By John F. Dovidio

Part II - The Building Blocks of a Study pp 177-266

9 - participant recruitment pp 179-201.

• By Jesse Chandler

10 - Informed Consent to Research pp 202-223

• By David S. Festinger , Karen L. Dugosh , Hannah R. Callahan , Rachel A. Hough

11 - Experimenter Effects pp 224-243

• By Jocelyn Parong , Mariya Vodyanyk , C. Shawn Green , Susanne M. Jaeggi , Aaron R. Seitz

12 - Debriefing and Post-Experimental Procedures pp 244-266

• By Travis D. Clark , Ginette Blackhart

Part III - Data Collection pp 267-440

13 - cross-sectional studies pp 269-291.

• By Maninder Singh Setia

14 - Quasi-Experimental Research pp 292-313

• By Charles S. Reichardt , Daniel Storage , Damon Abraham

15 - Non-equivalent Control Group Pretest–Posttest Design in Social and Behavioral Research pp 314-332

• By Margaret Denny , Suzanne Denieffe , Kathleen O’Sullivan

16 - Experimental Methods pp 333-356

• By Thomas F. Denson , Craig A. Anderson

17 - Longitudinal Research: A World to Explore pp 357-377

• By Elisabetta Ruspini

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Research methods--quantitative, qualitative, and more: overview.

• Quantitative Research
• Qualitative Research
• Data Science Methods (Machine Learning, AI, Big Data)
• Text Mining and Computational Text Analysis
• Evidence Synthesis/Systematic Reviews
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About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. 

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more.  This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question. 

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods.  HERE IS THE ONLINE GUIDE  to this one-stop shopping collection, and some helpful links are below:

• SAGE Research Methods
• Little Green Books  (Quantitative Methods)
• Little Blue Books  (Qualitative Methods)
• Dictionaries and Encyclopedias  
• Case studies of real research projects
• Sample datasets for hands-on practice
• Streaming video--see methods come to life
• Methodspace- -a community for researchers
• SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools !  From this link, check out pages for each of the following topics:  discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

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Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

• Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
• Wiley Stats Ref for background information on statistics topics
• Survey Documentation and Analysis (SDA) .  Program for easy web-based analysis of survey data.

Consultants

• D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
• Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
• Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

• IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
• OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
• Sponsored Projects Sponsored projects works with researchers applying for major external grants.
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• Last Updated: Sep 6, 2024 8:59 PM
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• Indian J Anaesth
• v.60(9); 2016 Sep

Basic statistical tools in research and data analysis

Zulfiqar ali.

Department of Anaesthesiology, Division of Neuroanaesthesiology, Sheri Kashmir Institute of Medical Sciences, Soura, Srinagar, Jammu and Kashmir, India

S Bala Bhaskar

1 Department of Anaesthesiology and Critical Care, Vijayanagar Institute of Medical Sciences, Bellary, Karnataka, India

Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.

INTRODUCTION

Statistics is a branch of science that deals with the collection, organisation, analysis of data and drawing of inferences from the samples to the whole population.[ 1 ] This requires a proper design of the study, an appropriate selection of the study sample and choice of a suitable statistical test. An adequate knowledge of statistics is necessary for proper designing of an epidemiological study or a clinical trial. Improper statistical methods may result in erroneous conclusions which may lead to unethical practice.[ 2 ]

Variable is a characteristic that varies from one individual member of population to another individual.[ 3 ] Variables such as height and weight are measured by some type of scale, convey quantitative information and are called as quantitative variables. Sex and eye colour give qualitative information and are called as qualitative variables[ 3 ] [ Figure 1 ].

Classification of variables

Quantitative variables

Quantitative or numerical data are subdivided into discrete and continuous measurements. Discrete numerical data are recorded as a whole number such as 0, 1, 2, 3,… (integer), whereas continuous data can assume any value. Observations that can be counted constitute the discrete data and observations that can be measured constitute the continuous data. Examples of discrete data are number of episodes of respiratory arrests or the number of re-intubations in an intensive care unit. Similarly, examples of continuous data are the serial serum glucose levels, partial pressure of oxygen in arterial blood and the oesophageal temperature.

A hierarchical scale of increasing precision can be used for observing and recording the data which is based on categorical, ordinal, interval and ratio scales [ Figure 1 ].

Categorical or nominal variables are unordered. The data are merely classified into categories and cannot be arranged in any particular order. If only two categories exist (as in gender male and female), it is called as a dichotomous (or binary) data. The various causes of re-intubation in an intensive care unit due to upper airway obstruction, impaired clearance of secretions, hypoxemia, hypercapnia, pulmonary oedema and neurological impairment are examples of categorical variables.

Ordinal variables have a clear ordering between the variables. However, the ordered data may not have equal intervals. Examples are the American Society of Anesthesiologists status or Richmond agitation-sedation scale.

Interval variables are similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced. A good example of an interval scale is the Fahrenheit degree scale used to measure temperature. With the Fahrenheit scale, the difference between 70° and 75° is equal to the difference between 80° and 85°: The units of measurement are equal throughout the full range of the scale.

Ratio scales are similar to interval scales, in that equal differences between scale values have equal quantitative meaning. However, ratio scales also have a true zero point, which gives them an additional property. For example, the system of centimetres is an example of a ratio scale. There is a true zero point and the value of 0 cm means a complete absence of length. The thyromental distance of 6 cm in an adult may be twice that of a child in whom it may be 3 cm.

STATISTICS: DESCRIPTIVE AND INFERENTIAL STATISTICS

Descriptive statistics[ 4 ] try to describe the relationship between variables in a sample or population. Descriptive statistics provide a summary of data in the form of mean, median and mode. Inferential statistics[ 4 ] use a random sample of data taken from a population to describe and make inferences about the whole population. It is valuable when it is not possible to examine each member of an entire population. The examples if descriptive and inferential statistics are illustrated in Table 1 .

Example of descriptive and inferential statistics

Descriptive statistics

The extent to which the observations cluster around a central location is described by the central tendency and the spread towards the extremes is described by the degree of dispersion.

Measures of central tendency

The measures of central tendency are mean, median and mode.[ 6 ] Mean (or the arithmetic average) is the sum of all the scores divided by the number of scores. Mean may be influenced profoundly by the extreme variables. For example, the average stay of organophosphorus poisoning patients in ICU may be influenced by a single patient who stays in ICU for around 5 months because of septicaemia. The extreme values are called outliers. The formula for the mean is

where x = each observation and n = number of observations. Median[ 6 ] is defined as the middle of a distribution in a ranked data (with half of the variables in the sample above and half below the median value) while mode is the most frequently occurring variable in a distribution. Range defines the spread, or variability, of a sample.[ 7 ] It is described by the minimum and maximum values of the variables. If we rank the data and after ranking, group the observations into percentiles, we can get better information of the pattern of spread of the variables. In percentiles, we rank the observations into 100 equal parts. We can then describe 25%, 50%, 75% or any other percentile amount. The median is the 50 th percentile. The interquartile range will be the observations in the middle 50% of the observations about the median (25 th -75 th percentile). Variance[ 7 ] is a measure of how spread out is the distribution. It gives an indication of how close an individual observation clusters about the mean value. The variance of a population is defined by the following formula:

where σ 2 is the population variance, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The variance of a sample is defined by slightly different formula:

where s 2 is the sample variance, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. The formula for the variance of a population has the value ‘ n ’ as the denominator. The expression ‘ n −1’ is known as the degrees of freedom and is one less than the number of parameters. Each observation is free to vary, except the last one which must be a defined value. The variance is measured in squared units. To make the interpretation of the data simple and to retain the basic unit of observation, the square root of variance is used. The square root of the variance is the standard deviation (SD).[ 8 ] The SD of a population is defined by the following formula:

where σ is the population SD, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The SD of a sample is defined by slightly different formula:

where s is the sample SD, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. An example for calculation of variation and SD is illustrated in Table 2 .

Example of mean, variance, standard deviation

Normal distribution or Gaussian distribution

Most of the biological variables usually cluster around a central value, with symmetrical positive and negative deviations about this point.[ 1 ] The standard normal distribution curve is a symmetrical bell-shaped. In a normal distribution curve, about 68% of the scores are within 1 SD of the mean. Around 95% of the scores are within 2 SDs of the mean and 99% within 3 SDs of the mean [ Figure 2 ].

Normal distribution curve

Skewed distribution

It is a distribution with an asymmetry of the variables about its mean. In a negatively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the right of Figure 1 . In a positively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the left of the figure leading to a longer right tail.

Curves showing negatively skewed and positively skewed distribution

Inferential statistics

In inferential statistics, data are analysed from a sample to make inferences in the larger collection of the population. The purpose is to answer or test the hypotheses. A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. Hypothesis tests are thus procedures for making rational decisions about the reality of observed effects.

Probability is the measure of the likelihood that an event will occur. Probability is quantified as a number between 0 and 1 (where 0 indicates impossibility and 1 indicates certainty).

In inferential statistics, the term ‘null hypothesis’ ( H 0 ‘ H-naught ,’ ‘ H-null ’) denotes that there is no relationship (difference) between the population variables in question.[ 9 ]

Alternative hypothesis ( H 1 and H a ) denotes that a statement between the variables is expected to be true.[ 9 ]

The P value (or the calculated probability) is the probability of the event occurring by chance if the null hypothesis is true. The P value is a numerical between 0 and 1 and is interpreted by researchers in deciding whether to reject or retain the null hypothesis [ Table 3 ].

P values with interpretation

If P value is less than the arbitrarily chosen value (known as α or the significance level), the null hypothesis (H0) is rejected [ Table 4 ]. However, if null hypotheses (H0) is incorrectly rejected, this is known as a Type I error.[ 11 ] Further details regarding alpha error, beta error and sample size calculation and factors influencing them are dealt with in another section of this issue by Das S et al .[ 12 ]

Illustration for null hypothesis

PARAMETRIC AND NON-PARAMETRIC TESTS

Numerical data (quantitative variables) that are normally distributed are analysed with parametric tests.[ 13 ]

Two most basic prerequisites for parametric statistical analysis are:

• The assumption of normality which specifies that the means of the sample group are normally distributed
• The assumption of equal variance which specifies that the variances of the samples and of their corresponding population are equal.

However, if the distribution of the sample is skewed towards one side or the distribution is unknown due to the small sample size, non-parametric[ 14 ] statistical techniques are used. Non-parametric tests are used to analyse ordinal and categorical data.

Parametric tests

The parametric tests assume that the data are on a quantitative (numerical) scale, with a normal distribution of the underlying population. The samples have the same variance (homogeneity of variances). The samples are randomly drawn from the population, and the observations within a group are independent of each other. The commonly used parametric tests are the Student's t -test, analysis of variance (ANOVA) and repeated measures ANOVA.

Student's t -test

Student's t -test is used to test the null hypothesis that there is no difference between the means of the two groups. It is used in three circumstances:

where X = sample mean, u = population mean and SE = standard error of mean

where X 1 − X 2 is the difference between the means of the two groups and SE denotes the standard error of the difference.

• To test if the population means estimated by two dependent samples differ significantly (the paired t -test). A usual setting for paired t -test is when measurements are made on the same subjects before and after a treatment.

The formula for paired t -test is:

where d is the mean difference and SE denotes the standard error of this difference.

The group variances can be compared using the F -test. The F -test is the ratio of variances (var l/var 2). If F differs significantly from 1.0, then it is concluded that the group variances differ significantly.

Analysis of variance

The Student's t -test cannot be used for comparison of three or more groups. The purpose of ANOVA is to test if there is any significant difference between the means of two or more groups.

In ANOVA, we study two variances – (a) between-group variability and (b) within-group variability. The within-group variability (error variance) is the variation that cannot be accounted for in the study design. It is based on random differences present in our samples.

However, the between-group (or effect variance) is the result of our treatment. These two estimates of variances are compared using the F-test.

A simplified formula for the F statistic is:

where MS b is the mean squares between the groups and MS w is the mean squares within groups.

Repeated measures analysis of variance

As with ANOVA, repeated measures ANOVA analyses the equality of means of three or more groups. However, a repeated measure ANOVA is used when all variables of a sample are measured under different conditions or at different points in time.

As the variables are measured from a sample at different points of time, the measurement of the dependent variable is repeated. Using a standard ANOVA in this case is not appropriate because it fails to model the correlation between the repeated measures: The data violate the ANOVA assumption of independence. Hence, in the measurement of repeated dependent variables, repeated measures ANOVA should be used.

Non-parametric tests

When the assumptions of normality are not met, and the sample means are not normally, distributed parametric tests can lead to erroneous results. Non-parametric tests (distribution-free test) are used in such situation as they do not require the normality assumption.[ 15 ] Non-parametric tests may fail to detect a significant difference when compared with a parametric test. That is, they usually have less power.

As is done for the parametric tests, the test statistic is compared with known values for the sampling distribution of that statistic and the null hypothesis is accepted or rejected. The types of non-parametric analysis techniques and the corresponding parametric analysis techniques are delineated in Table 5 .

Analogue of parametric and non-parametric tests

Median test for one sample: The sign test and Wilcoxon's signed rank test

The sign test and Wilcoxon's signed rank test are used for median tests of one sample. These tests examine whether one instance of sample data is greater or smaller than the median reference value.

This test examines the hypothesis about the median θ0 of a population. It tests the null hypothesis H0 = θ0. When the observed value (Xi) is greater than the reference value (θ0), it is marked as+. If the observed value is smaller than the reference value, it is marked as − sign. If the observed value is equal to the reference value (θ0), it is eliminated from the sample.

If the null hypothesis is true, there will be an equal number of + signs and − signs.

The sign test ignores the actual values of the data and only uses + or − signs. Therefore, it is useful when it is difficult to measure the values.

Wilcoxon's signed rank test

There is a major limitation of sign test as we lose the quantitative information of the given data and merely use the + or – signs. Wilcoxon's signed rank test not only examines the observed values in comparison with θ0 but also takes into consideration the relative sizes, adding more statistical power to the test. As in the sign test, if there is an observed value that is equal to the reference value θ0, this observed value is eliminated from the sample.

Wilcoxon's rank sum test ranks all data points in order, calculates the rank sum of each sample and compares the difference in the rank sums.

Mann-Whitney test

It is used to test the null hypothesis that two samples have the same median or, alternatively, whether observations in one sample tend to be larger than observations in the other.

Mann–Whitney test compares all data (xi) belonging to the X group and all data (yi) belonging to the Y group and calculates the probability of xi being greater than yi: P (xi > yi). The null hypothesis states that P (xi > yi) = P (xi < yi) =1/2 while the alternative hypothesis states that P (xi > yi) ≠1/2.

Kolmogorov-Smirnov test

The two-sample Kolmogorov-Smirnov (KS) test was designed as a generic method to test whether two random samples are drawn from the same distribution. The null hypothesis of the KS test is that both distributions are identical. The statistic of the KS test is a distance between the two empirical distributions, computed as the maximum absolute difference between their cumulative curves.

Kruskal-Wallis test

The Kruskal–Wallis test is a non-parametric test to analyse the variance.[ 14 ] It analyses if there is any difference in the median values of three or more independent samples. The data values are ranked in an increasing order, and the rank sums calculated followed by calculation of the test statistic.

Jonckheere test

In contrast to Kruskal–Wallis test, in Jonckheere test, there is an a priori ordering that gives it a more statistical power than the Kruskal–Wallis test.[ 14 ]

Friedman test

The Friedman test is a non-parametric test for testing the difference between several related samples. The Friedman test is an alternative for repeated measures ANOVAs which is used when the same parameter has been measured under different conditions on the same subjects.[ 13 ]

Tests to analyse the categorical data

Chi-square test, Fischer's exact test and McNemar's test are used to analyse the categorical or nominal variables. The Chi-square test compares the frequencies and tests whether the observed data differ significantly from that of the expected data if there were no differences between groups (i.e., the null hypothesis). It is calculated by the sum of the squared difference between observed ( O ) and the expected ( E ) data (or the deviation, d ) divided by the expected data by the following formula:

A Yates correction factor is used when the sample size is small. Fischer's exact test is used to determine if there are non-random associations between two categorical variables. It does not assume random sampling, and instead of referring a calculated statistic to a sampling distribution, it calculates an exact probability. McNemar's test is used for paired nominal data. It is applied to 2 × 2 table with paired-dependent samples. It is used to determine whether the row and column frequencies are equal (that is, whether there is ‘marginal homogeneity’). The null hypothesis is that the paired proportions are equal. The Mantel-Haenszel Chi-square test is a multivariate test as it analyses multiple grouping variables. It stratifies according to the nominated confounding variables and identifies any that affects the primary outcome variable. If the outcome variable is dichotomous, then logistic regression is used.

SOFTWARES AVAILABLE FOR STATISTICS, SAMPLE SIZE CALCULATION AND POWER ANALYSIS

Numerous statistical software systems are available currently. The commonly used software systems are Statistical Package for the Social Sciences (SPSS – manufactured by IBM corporation), Statistical Analysis System ((SAS – developed by SAS Institute North Carolina, United States of America), R (designed by Ross Ihaka and Robert Gentleman from R core team), Minitab (developed by Minitab Inc), Stata (developed by StataCorp) and the MS Excel (developed by Microsoft).

There are a number of web resources which are related to statistical power analyses. A few are:

• StatPages.net – provides links to a number of online power calculators
• G-Power – provides a downloadable power analysis program that runs under DOS
• Power analysis for ANOVA designs an interactive site that calculates power or sample size needed to attain a given power for one effect in a factorial ANOVA design
• SPSS makes a program called SamplePower. It gives an output of a complete report on the computer screen which can be cut and paste into another document.

It is important that a researcher knows the concepts of the basic statistical methods used for conduct of a research study. This will help to conduct an appropriately well-designed study leading to valid and reliable results. Inappropriate use of statistical techniques may lead to faulty conclusions, inducing errors and undermining the significance of the article. Bad statistics may lead to bad research, and bad research may lead to unethical practice. Hence, an adequate knowledge of statistics and the appropriate use of statistical tests are important. An appropriate knowledge about the basic statistical methods will go a long way in improving the research designs and producing quality medical research which can be utilised for formulating the evidence-based guidelines.

Get Citation

The seventh edition of Research Methods and Statistics in Psychology provides students with the most readable and comprehensive survey of research methods, statistical concepts and procedures in psychology today. Assuming no prior knowledge, this bestselling text takes you through every stage of your research project giving advice on planning and conducting studies, analysing data and writing up reports.

The book provides clear coverage of experimental, interviewing and observational methods, psychological testing, qualitative methods and analysis and statistical procedures which include nominal level tests, multi-factorial ANOVA designs, multiple regression, log linear analysis, and factor analysis.　It features detailed and illustrated SPSS instructions for all these and other procedures, eliminating the need for an extra SPSS textbook.

New features to this edition include:

• Additional coverage of factor analysis and online and modern research methods
• Expanded coverage of report writing guidelines
• References updated throughout
• Presentation updated throughout, to include more figures, tables and full colour to help break up the text
• Companion website signposted throughout the book to improve student usability
• Improved and extended web links and further reading associated with every chapter.

Each chapter contains a glossary, key terms and newly integrated exercises, ensuring that key concepts are understood. A fully updated companion website ( www.routledge.com/cw/coolican ) provides additional exercises, testbanks for each chapter, revision flash cards, links to further reading and data for use with SPSS.

TABLE OF CONTENTS

Part 1 | 322  pages, research methods and ethics, chapter 1 | 30  pages, psychology, science and research, chapter 2 | 34  pages, measuring people – variables, samples and the qualitative critique, chapter 3 | 30  pages, experiments and experimental designs in psychology, chapter 4 | 33  pages, validity in psychological research, chapter 5 | 19  pages, quasi-experiments and non-experiments, chapter 6 | 31  pages, observational methods – watching and being with people, chapter 7 | 30  pages, interview methods – asking people direct questions, chapter 8 | 36  pages, psychological tests and measurement scales, chapter 9 | 18  pages, comparison studies – cross-sectional, longitudinal and cross-cultural studies, chapter 10 | 31  pages, qualitative approaches in psychology, chapter 11 | 24  pages, ethical issues in psychological research, part 2 | 434  pages, analysing data and writing reports, chapter 12 | 34  pages, analysing qualitative data, chapter 13 | 40  pages, statistics – organising the data, chapter 14 | 18  pages, graphical representation of data, chapter 15 | 19  pages, frequencies and distributions, chapter 16 | 31  pages, significance testing – was it a real effect, chapter 17 | 51  pages, testing for differences between two samples, chapter 18 | 32  pages, tests for categorical variables and frequency tables, chapter 19 | 36  pages, correlation, chapter 20 | 16  pages, regression and multiple regression, chapter 21 | 20  pages, factor analysis, chapter 22 | 28  pages, multi-level analysis – differences between more than two conditions (anova), chapter 23 | 19  pages, multi-factorial anova designs, chapter 24 | 24  pages, anova for repeated measures designs, chapter 25 | 14  pages, choosing a significance test for your data, chapter 26 | 46  pages, planning your practical and writing up your report.

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• Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

• The type of knowledge you aim to produce
• The type of data you will collect and analyze
• The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

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The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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Top 50 Statistics Project Ideas [Revised]

• Post author By admin
• April 23, 2024

Welcome, curious minds! Today, we’re diving into the exciting world of statistics projects. Now, before you let out a groan thinking about boring numbers, let me tell you something – statistics can be fun, useful, and even eye-opening! Whether you’re a student looking for a cool project or just someone intrigued by the power of numbers, stick around. We’re going to explore different types of statistics project ideas you can try out.

Table of Contents

Factors to Consider When Choosing a Project

So, you’re ready to embark on a statistics project adventure. Before you jump in, it’s essential to consider a few key factors. These considerations will not only help you choose the right project but also ensure a smoother journey from start to finish.

• Interest and Relevance
• Interest: First and foremost, pick a topic that genuinely interests you. Passion drives motivation, and when you’re excited about a subject, the project becomes more enjoyable.
• Relevance: Consider the real-world relevance of your project. Is it something that has practical applications? Perhaps it’s an issue in your community, a challenge in your field of study, or a topic you’ve always been curious about.
• Available Data
• Data Access: Do you have access to the data you need? It could be public datasets, surveys you conduct, or information from your workplace or school.
• Data Quality: Ensure the data you’re working with is reliable and of good quality. Poor-quality data can lead to inaccurate conclusions.
• Complexity and Feasibility
• Start Simple: Especially if you’re new to statistics projects, it’s wise to start with something manageable. Overly complex projects can be overwhelming and may not be completed successfully.
• Resources: Consider the resources you have at your disposal. This includes time, software, access to experts or mentors, and any other tools you’ll need.
• Potential Impact or Contribution
• Who Benefits: Think about who could benefit from your project. Is it purely for academic purposes, or could it have real-world applications? Projects with tangible impacts can be incredibly rewarding.
• Contribution: Consider how your project fits into the larger picture. Could it contribute to existing research, shed light on an important issue, or offer insights that haven’t been explored before?
• Ethical Considerations
• Privacy and Consent: If your project involves human subjects or sensitive data, ensure you have proper consent and follow ethical guidelines.
• Bias Awareness: Be aware of potential biases in your data collection and analysis. Take steps to minimize biases and ensure fairness in your conclusions.
• Timeline and Scope
• Realistic Timeline: Be realistic about how much time you have to dedicate to the project. Consider deadlines and other commitments.
• Project Scope: Make sure you know exactly what your project is about. What questions are you trying to answer, and what do you hope to find out? This will help keep your project focused and manageable.
• Learning Objectives
• Skills Development: Consider what skills you want to develop through this project. Are you looking to improve your data analysis, presentation, or critical thinking skills?
• Learning Goals: Define clear learning goals. What do you hope to learn or discover through this project? Setting objectives will guide your work and help you stay on track.
• Feedback and Iteration
• Plan for Feedback: Consider how you’ll gather feedback throughout the project. This could be from peers, instructors, or experts in the field.
• Iterative Process: Understand that projects often evolve. Be open to making adjustments based on feedback and new insights that emerge during your analysis.

Top 50 Statistics Project Ideas: Category Wise

Health and medicine.

• Analyze patient recovery times for different treatments.
• Investigate the relationship between exercise frequency and heart health.
• Study the effectiveness of different diets on weight loss.
• Compare the prevalence of mental health disorders across age groups.
• Examine the impact of smoking on lung capacity using a controlled study.
• Analyze hospital readmission rates for specific conditions.

Business and Economics

• Conduct a market segmentation analysis for a new product.
• Analyze customer churn rates for a subscription-based service.
• Study the impact of advertising on product sales.
• Compare the financial performance of companies in different industries.
• Predict stock market trends using historical data.
• Analyze factors influencing employee satisfaction and productivity.

Social Sciences

• Investigate the relationship between income levels and voting patterns.
• Analyze survey data to understand public perception of climate change.
• Study crime rates and factors influencing crime in urban areas.
• Examine the impact of social media on interpersonal relationships.
• Analyze trends in education attainment across generations.
• Investigate the gender pay gap in a specific industry.

Environmental Studies

• Study the effects of pollution on respiratory health in a city.
• Analyze temperature trends to understand climate change in a region.
• Investigate the impact of deforestation on biodiversity.
• Study the effectiveness of recycling programs in reducing waste.
• Analyze water quality data from different sources (rivers, lakes, etc.).
• Investigate the relationship between air quality and asthma rates.
• Analyze standardized test scores to identify trends in student performance.
• Study the impact of class size on academic achievement.
• Investigate factors influencing student dropout rates.
• Analyze the effectiveness of different teaching methods on learning outcomes.
• Study the correlation between parental involvement and student success.
• Analyze trends in college acceptance rates over the years.

Psychology and Behavior

• Study the impact of social media use on self-esteem among teenagers.
• Analyze sleep patterns and their effects on cognitive performance.
• Investigate the correlation between stress levels and physical health.
• Study the effects of music on productivity in a workplace setting.
• Analyze factors influencing consumer purchasing decisions.
• Investigate the relationship between personality traits and career choices.

Technology and Data Analysis

• Analyze website traffic data to optimize user experience.
• Study the effectiveness of different spam filters in email systems.
• Investigate trends in mobile app usage across demographics.
• Analyze cybersecurity threats and vulnerabilities in a network.
• Study the impact of social media algorithms on content visibility.
• Analyze user reviews to identify trends and patterns in product satisfaction.

Demographics and Population Studies

• Study population growth and migration patterns in a specific region.
• Analyze demographic trends to predict future housing needs.
• Investigate the impact of aging populations on healthcare systems.
• Study the correlation between income levels and family size.
• Analyze trends in marriage and divorce rates over the years.
• Investigate factors influencing immigration patterns.

Sports and Fitness

• Analyze performance data to identify factors contributing to athletic success.
• Study the impact of different training programs on athlete performance.

How Do You Start A Statistics Project?

Starting a statistics project can seem daunting at first, but with a structured approach, it becomes manageable and even exciting. Here’s a step-by-step guide to help you kick off your statistics project:

Step 1: Define Your Objective

• Identify Your Interest: What topic interests you the most? Choose a subject that you’re curious about or passionate about.
• Define Your Goal: What do you want to achieve with this project? Are you trying to uncover trends, test a hypothesis, or make predictions?

Step 2: Formulate a Research Question

• Narrow Down Your Focus: Based on your objective, create a specific research question. It should be clear, concise, and focused.
• Example: “Does exercise frequency affect heart rate in adults over 50?”

Step 3: Gather Data

• Identify Data Sources: Determine where you’ll get your data. It could be from public datasets, surveys, experiments, or existing research.
• Collect Data: If you need to collect new data, design a methodical approach. For surveys, create clear questions. For experiments, plan your variables and controls.

Step 4: Clean and Prepare Your Data

• Data Cleaning: This is crucial. Remove errors, inconsistencies, and outliers from your dataset.
• Organize Data: Arrange your data in a format suitable for analysis. Use software like Excel, Python, R, or SPSS for this step.

Step 5: Choose Your Statistical Methods

• Select Appropriate Tests: Based on your research question and data type (continuous, categorical, etc.), choose the right statistical tests. Common tests include t-tests, ANOVA, regression, chi-square, etc.
• Consider Descriptive vs. Inferential: Decide if you’re focusing on descriptive statistics (summarizing data) or inferential statistics (making predictions or generalizations).

Step 6: Perform Analysis

• Run Your Tests: Use your chosen statistical software to run the tests.
• Interpret Results: Analyze the output. What do the numbers and graphs tell you? Do they support your hypothesis or research question?

Step 7: Create Visualizations

• Charts and Graphs: Create visual representations of your data . Bar charts, scatter plots, histograms, etc., can help convey your findings.
• Narrate Your Story: Explain what each visualization means in relation to your research question.

Step 8: Draw Conclusions

• Answer Your Research Question: Based on your analysis, what’s the answer to your research question?
• Discuss Implications: What do your findings mean? How do they contribute to the existing knowledge in the field?

Step 9: Document Your Process

• Write a Report: Document your entire process, from the research question to the conclusions. Include details about data sources, methods, and results.
• Include Citations: If you used external sources or datasets, cite them properly.
• Create Presentations: If needed, prepare a presentation to showcase your findings.

Step 10: Reflect and Iterate

• Reflect on Your Experience: What did you learn from this project? What would you do differently next time?
• Share Your Work: Present your project to peers, mentors, or teachers for feedback.
• Consider Next Steps: Does your project lead to further questions or investigations? Think about the next phase of research.
• Start Early: Give yourself plenty of time, especially for data collection and analysis.
• Stay Organized: Keep track of your data sources, methods, and analysis steps.
• Seek Help: If you’re stuck, don’t hesitate to ask for guidance from teachers, mentors, or online communities.
• Enjoy the Process: Statistics projects can be fascinating and rewarding. Embrace the journey of discovery!

Phew! We’ve covered a lot, haven’t we? Hopefully, this journey through statistics projects has shown you that numbers aren’t just for mathematicians in stuffy rooms. They’re tools we can all use to uncover truths, make decisions, and even change the world a bit.

So, whether you’re intrigued by the idea of predicting the stock market, exploring climate change data, or understanding why people love certain ice cream flavors, there are  statistics project ideas out there waiting for you. Go ahead, pick one that sparks your interest, gather some data, and let the numbers tell their story.

Remember, statistics isn’t just about math; it’s about curiosity, exploration, and making sense of the world around us. Happy analyzing!

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Home » 500+ Statistics Research Topics

500+ Statistics Research Topics

Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data . It is a fundamental tool used in various fields such as business, social sciences, engineering, healthcare, and many more. As a research topic , statistics can be a fascinating subject to explore, as it allows researchers to investigate patterns, trends, and relationships within data. With the help of statistical methods, researchers can make informed decisions and draw valid conclusions based on empirical evidence. In this post, we will explore some interesting statistics research topics that can be pursued by researchers to further expand our understanding of this field.

Statistics Research Topics

Statistics Research Topics are as follows:

• Analysis of the effectiveness of different marketing strategies on consumer behavior.
• An investigation into the relationship between economic growth and environmental sustainability.
• A study of the effects of social media on mental health and well-being.
• A comparative analysis of the educational outcomes of public and private schools.
• The impact of climate change on agriculture and food security.
• A survey of the prevalence and causes of workplace stress in different industries.
• A statistical analysis of crime rates in urban and rural areas.
• An evaluation of the effectiveness of alternative medicine treatments.
• A study of the relationship between income inequality and health outcomes.
• A comparative analysis of the effectiveness of different weight loss programs.
• An investigation into the factors that affect job satisfaction among employees.
• A statistical analysis of the relationship between poverty and crime.
• A study of the factors that influence the success of small businesses.
• A survey of the prevalence and causes of childhood obesity.
• An evaluation of the effectiveness of drug addiction treatment programs.
• A statistical analysis of the relationship between gender and leadership in organizations.
• A study of the relationship between parental involvement and academic achievement.
• An investigation into the causes and consequences of income inequality.
• A comparative analysis of the effectiveness of different types of therapy for mental health conditions.
• A survey of the prevalence and causes of substance abuse among teenagers.
• An evaluation of the effectiveness of online education compared to traditional classroom learning.
• A statistical analysis of the impact of globalization on different industries.
• A study of the relationship between social media use and political polarization.
• An investigation into the factors that influence customer loyalty in the retail industry.
• A comparative analysis of the effectiveness of different types of advertising.
• A survey of the prevalence and causes of workplace discrimination.
• An evaluation of the effectiveness of different types of employee training programs.
• A statistical analysis of the relationship between air pollution and health outcomes.
• A study of the factors that affect employee turnover rates.
• An investigation into the causes and consequences of income mobility.
• A comparative analysis of the effectiveness of different types of leadership styles.
• A survey of the prevalence and causes of mental health disorders among college students.
• An evaluation of the effectiveness of different types of cancer treatments.
• A statistical analysis of the impact of social media influencers on consumer behavior.
• A study of the factors that influence the adoption of renewable energy sources.
• An investigation into the relationship between alcohol consumption and health outcomes.
• A comparative analysis of the effectiveness of different types of conflict resolution strategies.
• A survey of the prevalence and causes of childhood poverty.
• An evaluation of the effectiveness of different types of diversity training programs.
• A statistical analysis of the relationship between immigration and economic growth.
• A study of the factors that influence customer satisfaction in the service industry.
• An investigation into the causes and consequences of urbanization.
• A comparative analysis of the effectiveness of different types of economic policies.
• A survey of the prevalence and causes of elder abuse.
• An evaluation of the effectiveness of different types of rehabilitation programs for prisoners.
• A statistical analysis of the impact of automation on different industries.
• A study of the factors that influence employee productivity in the workplace.
• An investigation into the causes and consequences of gentrification.
• A comparative analysis of the effectiveness of different types of humanitarian aid.
• A survey of the prevalence and causes of homelessness.
• Exploring the relationship between socioeconomic status and access to healthcare services
• An analysis of the relationship between parental education level and children’s academic performance.
• Exploring the effects of different statistical models on prediction accuracy in machine learning.
• The Impact of Social Media on Consumer Behavior: A Statistical Analysis
• Bayesian hierarchical modeling for network data analysis
• Spatial statistics and modeling for environmental data
• Nonparametric methods for time series analysis
• Bayesian inference for high-dimensional data analysis
• Multivariate analysis for genetic data
• Machine learning methods for predicting financial markets
• Causal inference in observational studies
• Sampling design and estimation for complex surveys
• Robust statistical methods for outlier detection
• Statistical inference for large-scale simulations
• Survival analysis and its applications in medical research
• Mixture models for clustering and classification
• Time-varying coefficient models for longitudinal data
• Multilevel modeling for complex data structures
• Graphical modeling and Bayesian networks
• Experimental design for clinical trials
• Inference for network data using stochastic block models
• Nonlinear regression modeling for data with complex structures
• Statistical learning for social network analysis
• Time series forecasting using deep learning methods
• Model selection and variable importance in high-dimensional data
• Spatial point process modeling for environmental data
• Bayesian spatial modeling for disease mapping
• Functional data analysis for longitudinal studies
• Bayesian network meta-analysis
• Statistical methods for big data analysis
• Mixed-effects models for longitudinal data
• Clustering algorithms for text data
• Bayesian modeling for spatiotemporal data
• Multivariate analysis for ecological data
• Statistical analysis of genomic data
• Bayesian network inference for gene regulatory networks
• Principal component analysis for high-dimensional data
• Time series analysis of financial data
• Multivariate survival analysis for complex outcomes
• Nonparametric estimation of causal effects
• Bayesian network analysis of complex systems
• Statistical inference for multilevel network data
• Generalized linear mixed models for non-normal data
• Bayesian inference for dynamic systems
• Latent variable modeling for categorical data
• Statistical inference for social network data
• Regression models for panel data
• Bayesian spatiotemporal modeling for climate data
• Predictive modeling for customer behavior analysis
• Nonlinear time series analysis for ecological systems
• Statistical modeling for image analysis
• Bayesian hierarchical modeling for longitudinal data
• Network-based clustering for high-dimensional data
• Bayesian spatial modeling for ecological systems.
• Analysis of the Effect of Climate Change on Crop Yields: A Case Study
• Examining the Relationship Between Physical Activity and Mental Health in Young Adults
• A Comparative Study of Crime Rates in Urban and Rural Areas Using Statistical Methods
• Investigating the Effect of Online Learning on Student Performance in Mathematics
• A Statistical Analysis of the Relationship Between Economic Growth and Environmental Sustainability
• Evaluating the Effectiveness of Different Marketing Strategies for E-commerce Businesses
• Identifying the Key Factors Affecting Customer Loyalty in the Hospitality Industry
• An Analysis of the Factors Influencing Student Dropout Rates in Higher Education
• Examining the Impact of Gender on Salary Disparities in the Workplace Using Statistical Methods
• Investigating the Relationship Between Physical Fitness and Academic Performance in High School Students
• Analyzing the Effect of Social Support on Mental Health in Elderly Populations
• A Comparative Study of Different Methods for Forecasting Stock Prices
• Investigating the Effect of Online Reviews on Consumer Purchasing Decisions
• Identifying the Key Factors Affecting Employee Turnover Rates in the Technology Industry
• Analyzing the Effect of Advertising on Brand Awareness and Purchase Intentions
• A Study of the Relationship Between Health Insurance Coverage and Healthcare Utilization
• Examining the Effect of Parental Involvement on Student Achievement in Elementary School
• Investigating the Impact of Social Media on Political Campaigns Using Statistical Methods
• A Comparative Analysis of Different Methods for Detecting Fraud in Financial Transactions
• Analyzing the Relationship Between Entrepreneurial Characteristics and Business Success
• Investigating the Effect of Job Satisfaction on Employee Performance in the Service Industry
• Identifying the Key Factors Affecting the Adoption of Renewable Energy Technologies
• A Study of the Relationship Between Personality Traits and Academic Achievement
• Examining the Impact of Social Media on Body Image and Self-Esteem in Adolescents
• Investigating the Effect of Mobile Advertising on Consumer Behavior
• Analyzing the Relationship Between Healthcare Expenditures and Health Outcomes Using Statistical Methods
• A Comparative Study of Different Methods for Analyzing Customer Satisfaction Data
• Investigating the Impact of Economic Factors on Voter Behavior Using Statistical Methods
• Identifying the Key Factors Affecting Student Retention Rates in Community Colleges
• Analyzing the Relationship Between Workplace Diversity and Organizational Performance
• Investigating the Effect of Gamification on Learning and Motivation in Education
• A Study of the Relationship Between Social Support and Depression in Cancer Patients
• Examining the Impact of Technology on the Travel Industry Using Statistical Methods
• Investigating the Effect of Customer Service Quality on Customer Loyalty in the Retail Industry
• Analyzing the Relationship Between Internet Usage and Social Isolation in Older Adults
• A Comparative Study of Different Methods for Predicting Customer Churn in Telecommunications
• Investigating the Impact of Social Media on Consumer Attitudes Towards Brands Using Statistical Methods
• Identifying the Key Factors Affecting Student Success in Online Learning Environments
• Analyzing the Relationship Between Employee Engagement and Organizational Commitment
• Investigating the Effect of Customer Reviews on Sales in E-commerce Businesses
• A Study of the Relationship Between Political Ideology and Attitudes Towards Climate Change
• Examining the Impact of Technological Innovations on the Manufacturing Industry Using Statistical Methods
• Investigating the Effect of Social Support on Postpartum Depression in New Mothers
• Analyzing the Relationship Between Cultural Intelligence and Cross-Cultural Adaptation
• Investigating the relationship between socioeconomic status and health outcomes using statistical methods.
• Analyzing trends in crime rates and identifying factors that contribute to them using statistical methods.
• Examining the effectiveness of different advertising strategies using statistical analysis of consumer behavior.
• Identifying factors that influence voting behavior and election outcomes using statistical methods.
• Investigating the relationship between employee satisfaction and productivity in the workplace using statistical methods.
• Developing new statistical models to better understand the spread of infectious diseases.
• Analyzing the impact of climate change on global food production using statistical methods.
• Identifying patterns and trends in social media data using statistical methods.
• Investigating the relationship between social networks and mental health using statistical methods.
• Developing new statistical models to predict financial market trends and identify investment opportunities.
• Analyzing the effectiveness of different educational programs and interventions using statistical methods.
• Investigating the impact of environmental factors on public health using statistical methods.
• Developing new statistical models to analyze complex biological systems and identify new drug targets.
• Analyzing trends in consumer spending and identifying factors that influence buying behavior using statistical methods.
• Investigating the relationship between diet and health outcomes using statistical methods.
• Developing new statistical models to analyze gene expression data and identify biomarkers for disease.
• Analyzing patterns in crime data to predict future crime rates and improve law enforcement strategies.
• Investigating the effectiveness of different medical treatments using statistical methods.
• Developing new statistical models to analyze the impact of air pollution on public health.
• Analyzing trends in global migration and identifying factors that influence migration patterns using statistical methods.
• Investigating the impact of automation on the job market using statistical methods.
• Developing new statistical models to analyze climate data and predict future climate trends.
• Analyzing trends in online shopping behavior and identifying factors that influence consumer decisions using statistical methods.
• Investigating the impact of social media on political discourse using statistical methods.
• Developing new statistical models to analyze gene-environment interactions and identify new disease risk factors.
• Analyzing trends in the stock market and identifying factors that influence investment decisions using statistical methods.
• Investigating the impact of early childhood education on long-term academic and social outcomes using statistical methods.
• Developing new statistical models to analyze the relationship between human behavior and the environment.
• Analyzing trends in the use of renewable energy and identifying factors that influence adoption rates using statistical methods.
• Investigating the impact of immigration on labor market outcomes using statistical methods.
• Developing new statistical models to analyze the relationship between social determinants and health outcomes.
• Analyzing patterns in customer churn to predict future customer behavior and improve business strategies.
• Investigating the effectiveness of different marketing strategies using statistical methods.
• Developing new statistical models to analyze the relationship between air pollution and climate change.
• Analyzing trends in global tourism and identifying factors that influence travel behavior using statistical methods.
• Investigating the impact of social media on mental health using statistical methods.
• Developing new statistical models to analyze the impact of transportation on the environment.
• Analyzing trends in global trade and identifying factors that influence trade patterns using statistical methods.
• Investigating the impact of social networks on political participation using statistical methods.
• Developing new statistical models to analyze the relationship between climate change and biodiversity loss.
• Analyzing trends in the use of alternative medicine and identifying factors that influence adoption rates using statistical methods.
• Investigating the impact of technological change on the labor market using statistical methods.
• Developing new statistical models to analyze the impact of climate change on agriculture.
• Investigating the impact of social media on mental health: A longitudinal study.
• A comparison of the effectiveness of different types of teaching methods on student learning outcomes.
• Examining the relationship between sleep duration and productivity among college students.
• A study of the factors that influence employee job satisfaction in the tech industry.
• Analyzing the relationship between income level and health outcomes among low-income populations.
• Investigating the effectiveness of online learning platforms for high school students.
• A study of the factors that contribute to success in online entrepreneurship.
• Analyzing the impact of climate change on agricultural productivity in developing countries.
• A comparison of different statistical models for predicting stock market trends.
• Examining the impact of sports on mental health: A cross-sectional study.
• A study of the factors that influence employee retention in the hospitality industry.
• Analyzing the impact of cultural differences on international business negotiations.
• Investigating the effectiveness of different weight loss interventions for obese individuals.
• A study of the relationship between personality traits and academic achievement.
• Examining the impact of technology on job displacement: A longitudinal study.
• A comparison of the effectiveness of different types of advertising strategies on consumer behavior.
• Analyzing the impact of environmental regulations on corporate profitability.
• Investigating the effectiveness of different types of therapy for treating depression.
• A study of the factors that contribute to success in e-commerce.
• Examining the relationship between social support and mental health in the elderly population.
• A comparison of different statistical methods for analyzing complex survey data.
• Analyzing the impact of employee diversity on organizational performance.
• Investigating the effectiveness of different types of exercise for improving cardiovascular health.
• A study of the relationship between emotional intelligence and job performance.
• Examining the impact of work-life balance on employee well-being.
• A comparison of the effectiveness of different types of financial education programs for low-income populations.
• Analyzing the impact of air pollution on respiratory health in urban areas.
• Investigating the relationship between personality traits and leadership effectiveness.
• A study of the factors that influence consumer behavior in the luxury goods market.
• Examining the impact of social networks on political participation: A cross-sectional study.
• A comparison of different statistical methods for analyzing survival data.
• Analyzing the impact of government policies on income inequality.
• Investigating the effectiveness of different types of counseling for substance abuse.
• A study of the relationship between cultural values and consumer behavior.
• Examining the impact of technology on privacy: A longitudinal study.
• A comparison of the effectiveness of different types of online marketing strategies.
• Analyzing the impact of the gig economy on job satisfaction: A cross-sectional study.
• Investigating the effectiveness of different types of education interventions for improving financial literacy.
• A study of the factors that contribute to success in social entrepreneurship.
• Examining the impact of gender diversity on board performance in publicly-traded companies.
• A comparison of different statistical methods for analyzing panel data.
• Analyzing the impact of employee involvement in decision-making on organizational performance.
• Investigating the effectiveness of different types of treatment for anxiety disorders.
• A study of the relationship between cultural values and entrepreneurial success.
• Examining the impact of technology on the labor market: A longitudinal study.
• A comparison of the effectiveness of different types of direct mail campaigns.
• Analyzing the impact of telecommuting on employee productivity: A cross-sectional study.
• Investigating the effectiveness of different types of retirement planning interventions for low-income individuals.
• Analyzing the effectiveness of different educational interventions in improving student performance
• Investigating the impact of climate change on food production and food security
• Identifying factors that influence employee satisfaction and productivity in the workplace
• Examining the prevalence and causes of mental health disorders in different populations
• Evaluating the effectiveness of different marketing strategies in promoting consumer behavior
• Analyzing the prevalence and consequences of substance abuse in different communities
• Investigating the relationship between social media use and mental health outcomes
• Examining the role of genetics in the development of different diseases
• Identifying factors that contribute to the gender wage gap in different industries
• Analyzing the effectiveness of different policing strategies in reducing crime rates
• Investigating the impact of immigration on economic growth and development
• Examining the prevalence and causes of domestic violence in different populations
• Evaluating the effectiveness of different interventions for treating addiction
• Analyzing the prevalence and impact of childhood obesity on health outcomes
• Investigating the relationship between diet and chronic diseases such as diabetes and heart disease
• Examining the effects of different types of exercise on physical and mental health outcomes
• Identifying factors that influence voter behavior and political participation
• Analyzing the prevalence and impact of sleep disorders on health outcomes
• Investigating the effectiveness of different educational interventions in improving health outcomes
• Examining the impact of environmental pollution on public health outcomes
• Evaluating the effectiveness of different interventions for reducing opioid addiction and overdose rates
• Analyzing the prevalence and causes of homelessness in different communities
• Investigating the relationship between race and health outcomes
• Examining the impact of social support networks on health outcomes
• Identifying factors that contribute to income inequality in different regions
• Analyzing the prevalence and impact of workplace stress on employee health outcomes
• Investigating the relationship between education and income levels in different communities
• Examining the effects of different types of technology on mental health outcomes
• Evaluating the effectiveness of different interventions for reducing healthcare costs
• Analyzing the prevalence and impact of chronic pain on health outcomes
• Investigating the relationship between urbanization and public health outcomes
• Examining the effects of different types of drugs on health outcomes
• Identifying factors that contribute to educational attainment in different populations
• Analyzing the prevalence and causes of food insecurity in different communities
• Investigating the relationship between race and crime rates
• Examining the impact of social media on political participation and engagement
• Evaluating the effectiveness of different interventions for reducing poverty levels
• Analyzing the prevalence and impact of stress on mental health outcomes
• Investigating the relationship between religion and health outcomes
• Examining the effects of different types of parenting styles on child development outcomes
• Identifying factors that contribute to political polarization in different regions
• Analyzing the prevalence and causes of teenage pregnancy in different communities
• Investigating the impact of globalization on economic growth and development
• Examining the prevalence and impact of social isolation on mental health outcomes
• Evaluating the effectiveness of different interventions for reducing gun violence
• Analyzing the prevalence and impact of bullying on mental health outcomes
• Investigating the relationship between immigration and crime rates
• Examining the effects of different types of diets on health outcomes
• Identifying factors that contribute to social inequality in different regions
• Bayesian inference for high-dimensional models
• Analysis of longitudinal data with missing values
• Nonparametric regression with functional predictors
• Estimation and inference for copula models
• Statistical methods for neuroimaging data analysis
• Robust methods for high-dimensional data analysis
• Analysis of spatially correlated data
• Bayesian nonparametric modeling
• Statistical methods for network data
• Optimal experimental design for nonlinear models
• Multivariate time series analysis
• Inference for partially identified models
• Statistical learning for personalized medicine
• Statistical inference for rare events
• High-dimensional mediation analysis
• Analysis of multi-omics data
• Nonparametric regression with mixed types of predictors
• Estimation and inference for graphical models
• Statistical inference for infectious disease dynamics
• Robust methods for high-dimensional covariance matrix estimation
• Analysis of spatio-temporal data
• Bayesian modeling for ecological data
• Multivariate spatial point pattern analysis
• Statistical methods for functional magnetic resonance imaging (fMRI) data
• Nonparametric estimation of conditional distributions
• Statistical methods for spatial econometrics
• Inference for stochastic processes
• Bayesian spatiotemporal modeling
• High-dimensional causal inference
• Analysis of data from complex survey designs
• Bayesian nonparametric survival analysis
• Statistical methods for fMRI connectivity analysis
• Spatial quantile regression
• Statistical modeling for climate data
• Estimation and inference for item response models
• Bayesian model selection and averaging
• High-dimensional principal component analysis
• Analysis of data from clinical trials with noncompliance
• Nonparametric regression with censored data
• Statistical methods for functional data analysis
• Inference for network models
• Bayesian nonparametric clustering
• High-dimensional classification
• Analysis of ecological network data
• Statistical modeling for time-to-event data with multiple events
• Estimation and inference for nonparametric density estimation
• Bayesian nonparametric regression with time-varying coefficients
• Statistical methods for functional magnetic resonance spectroscopy (fMRS) data

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155 Best Statistics Project Topics for College Students

Are you a college student seeking an exciting project that blends your love for numbers with real-world impact? Your search ends here! Statistics projects are your gateway to unlock the power of data analysis and make a difference. The first step? Selecting the perfect project topic. It’s the foundation of your success. 

In this blog, we’ve made it easy for you. We’ve compiled a list of the best statistics project topics for college students, ensuring you have a wealth of options to choose from. Let’s dive into the world of statistics and find the ideal project that’ll make your academic journey truly remarkable.

Table of Contents

What are Statistics Topics?

Statistics topics encompass a wide range of subjects within the field of data analysis. These topics involve the collection, interpretation, and presentation of numerical data to draw meaningful conclusions. Some common statistics topics include data analysis, hypothesis testing, regression analysis, predictive modeling, and more. These topics are applied in various fields such as finance, healthcare, sports, psychology, and environmental science, to name a few. Statistics project topics for college students help researchers and analysts make informed decisions, solve real-world problems, and uncover patterns and trends within data, making them a fundamental aspect of academic and practical research.

Why Choose the Right Statistics Project Topic?

Before we dive into the list of statistics project topics for college students, you need to know the importance of choosing the project topics of statistics. Choosing the right statistics project topic is of paramount importance for several reasons:

• Relevance: A well-chosen topic ensures that your project aligns with your academic and career goals.
• Motivation: Selecting a topic that genuinely interests you keeps you motivated throughout the project.
• Data Availability: It ensures that there is sufficient data available for analysis, preventing potential roadblocks.
• Real-World Impact: A carefully chosen topic can lead to practical applications and contribute to solving real-world problems.
• Academic Success: The right topic increases the likelihood of academic success, leading to higher grades and a stronger understanding of statistical concepts.
• Career Opportunities: A project aligned with your interests can open doors to career opportunities in your chosen field.
• Personal Growth: It allows you to grow as a statistician or data analyst, gaining valuable skills and experience.

Also Read: Best Project Ideas for Software Engineering

List of Statistics Project Topics for College Students

Here is a complete list of statistics project topics for college students in 2023:

Descriptive Statistics

• Mean, Median, and Mode Analysis in Different Datasets
• Variance and Standard Deviation Comparison in Various Fields
• Exploring Measures of Central Tendency in Finance
• Analyzing Data Skewness and Kurtosis
• Quartile and Percentile Analysis in Health Data
• Frequency Distribution of Crime Rates in Different Regions
• Interquartile Range Examination in Educational Data
• Comparative Study of Dispersion in Sales Data
• Histogram Analysis for Population Growth
• Time Series Analysis of Temperature Data
• Measures of Spread in Sports Statistics
• Analysis of Wealth Distribution using Box Plots
• Exploring Descriptive Statistics in Environmental Data
• Examining Data Distribution in Political Surveys
• Analyzing Income Inequality using Gini Coefficient
• Correlation and Covariance in Social Sciences

Hypothesis Testing

• Testing the Gender Pay Gap Hypothesis
• T-Test Analysis of Educational Interventions
• Chi-Square Analysis in Healthcare Outcomes
• ANOVA Testing in Market Research
• Z-Test for Hypothesis in Retail Data
• Paired T-Test for Employee Productivity
• Wilcoxon Rank-Sum Test in Customer Satisfaction
• McNemar’s Test in Social Media Usage
• Kruskal-Wallis Test for Regional Sales Comparison
• Mann-Whitney U Test in Product Preferences
• Two-Proportion Z-Test in Voting Behavior
• Poisson Test in Accident Frequency
• Testing the Null Hypothesis in Quality Control
• Analysis of Correlation Significance in Marriage Age
• Hypothesis Testing in Criminal Justice Reform
• A/B Testing for Website Conversion Rates

Regression Analysis

• Simple Linear Regression in Predicting House Prices
• Multiple Regression Analysis in Car Mileage
• Logistic Regression for Credit Risk Assessment
• Polynomial Regression for Stock Market Prediction
• Ridge Regression in Environmental Impact Assessment
• Lasso Regression in Movie Box Office Predictions
• Time Series Forecasting with Exponential Smoothing
• ARIMA Modeling for Sales Forecasting
• Regression Trees for Customer Churn Prediction
• Analysis of Non-Linear Regression in Health Data
• Stepwise Regression for Predicting Academic Success
• Poisson Regression in Traffic Accident Analysis
• Logistic Regression for Disease Diagnosis
• Hierarchical Regression in Employee Satisfaction
• Multiple Regression Analysis in Urban Development
• Quantile Regression in Income Prediction

Bayesian Statistics

• Bayesian Inference in Drug Efficacy Testing
• Bayesian Decision Theory in Investment Strategies
• Bayesian Updating in Weather Forecasting
• Bayesian Networks for Disease Outbreak Prediction
• Bayesian Parameter Estimation in Machine Learning
• Markov Chain Monte Carlo (MCMC) in Political Polling
• Bayesian Classification in Email Spam Filtering
• Bayesian Optimization for Hyperparameter Tuning
• Bayesian Survival Analysis in Medical Research
• Bayesian Econometrics in Economic Forecasting
• Bayesian Analysis of Social Network Data
• Bayesian Belief Networks in Fraud Detection
• Bayesian Time Series Analysis in Financial Markets
• Bayesian Inference in Image Recognition
• Bayesian Spatial Analysis for Crime Prediction
• Bayesian Meta-Analysis in Clinical Trials

Experimental Design

• Factorial Design in Manufacturing Process Optimization
• Randomized Controlled Trials in Healthcare Interventions
• Latin Square Design in Agricultural Experiments
• Split-Plot Design for Quality Control
• Response Surface Methodology in Product Development
• Completely Randomized Design in Education Assessment
• Block Design for Agricultural Field Trials
• Fractional Factorial Design in Chemical Engineering
• Cross-Over Design in Drug Testing
• Two-Level Factorial Design for Marketing Campaigns
• Nested Design in Wildlife Ecology Studies
• Factorial ANOVA in Psychological Experiments
• Repeated Measures Design in Sports Performance Analysis
• Taguchi Design of Experiments in Engineering
• D-Optimal Design in Clinical Trials
• Central Composite Design for Food Process Optimization

Nonparametric Statistics

• Wilcoxon Signed-Rank Test in Employee Salaries
• Mann-Whitney U Test in Online Shopping Habits
• Kruskal-Wallis Test for Restaurant Ratings
• Spearman’s Rank Correlation in Social Media Metrics
• Friedman Test in Voting Preference Analysis
• Sign Test in Stock Price Movement
• Kendall’s Tau in Customer Satisfaction
• Anderson-Darling Test for Data Normality
• McNemar’s Test for Medical Diagnosis
• Kolmogorov-Smirnov Test in Marketing Analytics
• Nonparametric Regression Analysis in Real Estate
• The Hodges-Lehmann Estimator in Financial Data
• Nonparametric Tests for Time Series Data
• Mann-Whitney U Test in Product Reviews
• Mood’s Median Test in Consumer Preferences
• Comparing Nonparametric Tests in Various Fields

Multivariate Analysis

• Principal Component Analysis in Financial Risk Assessment
• Factor Analysis for Customer Satisfaction
• Canonical Correlation Analysis in Marketing Research
• Discriminant Analysis for Species Classification
• Cluster Analysis in Social Network Grouping
• Multidimensional Scaling for Image Similarity
• MANOVA in Psychological Assessment
• Redundancy Analysis in Environmental Impact Studies
• Structural Equation Modeling (SEM) for Education
• Canonical Discriminant Analysis in Healthcare Outcomes
• Correspondence Analysis for Political Surveys
• Path Analysis in Consumer Behavior
• Multiway Analysis in Image Compression
• Discriminant Analysis in Credit Scoring
• Cluster Analysis for Customer Segmentation
• Multivariate Time Series Analysis in Stock Prices

Survival Analysis

• Kaplan-Meier Survival Analysis in Cancer Studies
• Cox Proportional Hazards Model in Finance
• Log-Rank Test in Epidemiology
• Weibull Distribution in Engineering Reliability
• Parametric Survival Models in Pharmaceutical Trials
• Survival Analysis in Employee Retention
• Competing Risk Survival Analysis in Healthcare
• Bayesian Survival Analysis in Disease Progression
• Nonparametric Survival Analysis in Social Sciences
• Survival Analysis in Customer Churn
• Survival Analysis for Product Durability
• Time-Dependent Covariates in Survival Studies
• Frailty Models in Aging Research
• Cure Models in Medical Research
• Event History Analysis in Demography
• Survival Analysis of Wildlife Populations

Time Series Analysis

• Autocorrelation Function (ACF) and Partial ACF (PACF) Analysis
• Box-Jenkins Methodology for ARIMA Modeling
• Seasonal Decomposition of Time Series (STL)
• Exponential Smoothing Methods for Forecasting
• GARCH Models for Financial Volatility
• State Space Models for Economic Time Series
• Time Series Clustering Techniques
• Granger Causality Testing in Macroeconomics
• ARMA-GARCH Models in Stock Market Volatility
• Time Series Forecasting in Energy Consumption
• Wavelet Transform Analysis in Signal Processing
• Multivariate Time Series Forecasting in Supply Chain
• Long Short-Term Memory (LSTM) in Deep Learning
• Time Series Decomposition in Retail Sales
• Vector Autoregression (VAR) Models in Macroeconomic Analysis
• Time Series Analysis in Weather Forecasting

Machine Learning and Big Data

• Predictive Analytics using Machine Learning Algorithms
• Feature Selection Techniques in Big Data Analysis
• Random Forest Classification in Customer Churn Prediction
• Support Vector Machines (SVM) for Anomaly Detection
• Natural Language Processing (NLP) for Sentiment Analysis
• Clustering and Association Analysis in Market Basket Data
• Recommender Systems in E-commerce
• Deep Learning for Image Recognition
• Time Series Forecasting with Recurrent Neural Networks (RNN)
• Text Mining and Topic Modeling for Social Media Data
• Ensemble Learning Methods in Credit Scoring
• Big Data Analysis using Hadoop and Spark
• Classification and Regression Trees (CART) in Healthcare
• Unsupervised Learning for Customer Segmentation
• Machine Learning in Fraud Detection
• Dimensionality Reduction Techniques in High-Dimensional Data

These statistics project topics for college students should provide a diverse range of options for their statistics projects across various fields and methodologies.

How to Select the Perfect Statistics Project Topic?

Selecting the perfect statistics project topics for college students involves the following steps:

• Identify Your Interests: Choose a topic that genuinely interests you as it will keep you motivated throughout the project.
• Research Existing Data: Ensure that data related to your chosen topic is accessible and can be used for analysis.
• Define a Clear Objective: Clearly state the purpose of your project and the questions you aim to answer.
• Consult with Professors: Seek guidance from your professors to ensure the feasibility and relevance of your chosen topic.
• Consider Real-world Impact: Think about how your project can contribute to solving real-world problems or advancing a particular field.
• Plan Your Methodology: Outline the statistical techniques and tools you intend to use for analysis.
• Stay Organized: Keep detailed records of your work, data sources, and results to make the reporting phase easier.

In conclusion, the significance of selecting the right statistics project topics for college students cannot be overstated. It is the initial stride on your academic journey that sets the stage for a fulfilling and impactful experience. Fortunately, the diverse array of statistics project topics, spanning fields like sports, healthcare, finance, and psychology, ensures that there’s something for everyone. Your project is not merely an academic exercise but a chance to explore your passion and contribute meaningfully to your chosen area of study. By adhering to the steps outlined for topic selection, you can confidently venture into the world of statistics, where learning and discovery go hand in hand. So, choose wisely and embark on a statistical journey that promises both knowledge and fulfillment.

FAQs (Statistics Project Topics for College Students)

1. can i choose a statistics project topic outside my major.

Absolutely! Choosing a topic that interests you is more important than sticking to your major.

2. How do I access the necessary data for my project?

You can find datasets online, in academic libraries, or by collaborating with professionals in relevant fields.

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https://www.nist.gov/programs-projects/research-statistical-methods-project

Research on Statistical Methods Project

Since the formation of the Statistical Engineering Division in 1947, division staff, through their interdisciplinary research with NIST scientists and engineers, occasionally encounter problems that cannot be addressed using existing, or textbook, statistical methods. On such occasions, appropriate division staff conduct original research in mathematical and/or computational statistics, leading to new and more broadly applicable statistical methods. The division's unique contributions to the general methods of statistics tend to concentrate in areas where the measurement science activities at NIST present new challenges in planning and analyzing high precision data on high-accuracy measurement systems. So, many of the divisions original contributions fall into the following areas:

• Bayesian methods for metrology,
• statistical calibration and measurement assurance,
• experiment designs,
• components-of-variance estimation,
• methods for the design and analysis of interlaboratory comparisons, and
• computer intensive methods (bootstrap, permutation procedures, general distribution-free methods) and
• image analysis methods.

Description

The division typically produces one to a few publications on new statistical methods each year. A list of publications on some research from the past few years is shown below below.  Following the publication list, an example of current work on the development of novel statistical methods, useful both at NIST and elsewhere, is presented in more detail.

Major Accomplishments

The following are research publications written by SED staff and collaborators.

- Zhang, N. F. (2000). "Statistical Control Charts for Monitoring the Mean of a Stationary Process", to appear in the Journal of Statistical Computation and Simulation. - Vangel, M. G. and Rukhin, A. L. (1999). "Maximum-Likelihood Analysis for Heteroscedastic One-Way Random Effects ANOVA in Interlaboratory Studies," Biometrics, 55, 302-313. - Vangel, M. G. (1998). "ANOVA Estimates of Variance Components for Quasi-Balanced Mixed Models," Journal of Statistical Planning and Inference, 70, 139-148. - Rukhin, A. L. and Vangel, M. G. (1998). "Estimation of a Common Mean and Weighted Means Statistics, Journal of the American Statistical Association, 93, 303-309. - Zhang, N. F. (1998). "Estimating Process Capability Indices for Autocorrelated Data", Journal of Applied Statistics, 25(4), 559-574. - Zhang, N. F. (1998). "A Statistical Control Chart for Stationary Process Data," Technometrics, 40(1), 24-38. - Zhang, N. F. (1997), "Detection capability of residual control chart for stationary process data," Journal of Applied Statistics, 24(4), 475-492. - Wang, C. M. and Lam, C. T. (1997). "A mixed-effects model for the analysis of circular measurements," Technometrics, 39 (2), 119-126. - Brown, E. B., Iyer, H. K. and Wang, C. M. (1997). "Tolerance intervals for assessing individual bioequivalence," Statistics in Medicine, 16, 803-820. - Liu, H.K. (1997), "High-dimensional empirical linear prediction", Advanced Mathematical Tools in Metrology III, 79-90. - Filliben, J. J. and Li, K. C. (1997), "A systematic approach to the analysis of complex interaction patterns in two-level factorial design," Technometrics, 39 (3), 286-297. -  Wang, C. M. and Iyer, H. K. (1996). "Sampling plans for obtaining tolerance intervals in a balanced one-way random-effects model," Communications in Statistics - Theory and    Methods, 25 (2), 313-324. - Wang, C. M. and Lam, C. T. (1996). "Confidence limits for proportion of conformance," Journal of Quality Technology, 28 (4), 439-445. - Liao, C. T., Vecchia, D. F., and Iyer, H. K. (1996), "Construction of orthogonal two-level designs of user-specified resolution where N is not 2**k," Technometrics, 38, 342-353. - Vangel, M. G. (1996). "Confidence Intervals for a Normal Coefficient of Variation", The American Statistician, 15, 21-26. - Eberhardt, K. R. and Mee, R. W. (1996), "A comparison of uncertainty criteria for calibration," Technometrics, 38 (3), 221-229. - Kacker, R., N. F. Zhang and C. Hagwood (1996) "Real-time Control of a Measurement Process," Metrologia, 33, 433-445. - Coakley, K. J. (1996), "A Bootstrap Method for Nonlinear Filtering of EM-ML Reconstructions of PET Images," International Journal of Imaging Science and Technology, Vol. 7, 54-61. - Ahlbrandt, R. A., Vecchia, D. F., and Iyer, H. K. (1995), "Optimum design of serial measurement trees," Journal of Statistical Planning and Inference, 48, 379-390. - Zhang, N. F. and J. P. Pollard (1994) " Analysis of Autocorrelations in Dynamic Processes," Technometrics, 36, 354-368. - Wang, C. M. and Iyer, H. K. (1994). "Tolerance intervals for the distribution of true values in the presence of measurement errors," Technometrics, 36, 162-170. - Wang, C. M. (1994). "On estimating approximate degrees of freedom of chi-squared approximations," Communications in Statistics - Simulation and Computation, B23(3), 769-788. - Vangel, M. G. (1994). "One-Sided Nonparametric Tolerance Limits." Communications in Statistics: Simulation and Computation, 23, 1137. - Hsu, J. C., Hwang, J. T. G., Liu, H. K. and Rugreg, S. T. (1994). "Confidence intervals associated with tests for bioequivalence," Biometrika, 81 103-114. - Galambos, J. and Hagwood, C. (1994). "An unreliable Server Characterization on the Exponential Distribution," Journal of Applied Probability 31, 274-279. - Eberhardt, Keith R. and Mee, R. W. (1994). "Constant-Width Calibration Intervals for Linear Regression," Journal of Quality Technology, Vol 26, 21-29. - Wang, C. M. (1992). "Prediction intervals for a balanced one-way random-effects model," Communications in Statistics - Simulation and Computation, B21(3), 671-687. - Wang, C. M. (1992). "Approximate confidence intervals on linear combinations of expected mean squares," Journal of Statistical Computation and Simulation, 43, 229-241. - Vangel, M. G. (1992). "New Methods for One-Sided Tolerance Limits for a One-Way Balanced Random-Effects ANOVA Model," Technometrics, 34, 176-185. - Hwang, J. T. and Liu, H. K. (1992). "Existence and nonexistence theorems of finite diameter sequential confidence regions for errors-in-variables models," Statistics and Probability Letters 13, 45-55. - Galambos, J. and Hagwood, C. (1992). "The Characterization of Distribution Function by the Second Moments of the Residual Life," Commun. Statist.-Theory and Methods 21, 1463-1468. - Hagwood, C. (1992). "The Calibration Problem as an Ill-Posed Inverse Problem," Journal of Statistical Planning and Inference 31, 179-185. - Wang, C. M. (1991). "Approximate confidence intervals on positive linear combinations of expected mean squares," Communications in Statistics - Simulation and Computation, B20(1), 81-96. - Vecchia, D. F. and Iyer, H. K. (1991). "Exact moments of the quartic assignment statistic with an application to multiple regression," Communications in Statistics-Theory and Methods, 20, 3253-3269. - Mee, R. W., Eberhardt, Keith R., Reeve, C. P. (1991). "Calibration and Simultaneous Tolerance Intervals for Regression," Technometrics, Vol. 33, No. 2, pp. 211-220. - Schiller, S. and Eberhardt, Keith R. (1991). "Combining Data from Independent Chemical Analysis Methods," Spectrochimica Acta. - Coakley, K. J.  (1991), "A Cross-Validation Procedure for Stopping the EM Algorithm and Deconvolution of Neutron Depth Profiling Spectra," IEEE Transactions on Nuclear Science, Vol. 38, 9-16. - Zhang, N. F., G. A. Stenback and D. M. Wardrop (1990) "Interval Estimation of Process Capability Index," Communications In Statistics - Theory and Methods, 19(12), 4455-4470. - Zhang, N. F., (1990) "Group Delay, Partial Group delay and Index-lag relationship for Multidimensional Processes," Commun. In Statistics-Theory and Methods, 19(9), 3137-3145. - Wang, C. M. (1990). "On the lower bound of confidence coefficients for a confidence interval on variance components," Biometrics, 46, 187-192. - Wang, C. M. (1990). "On ranges of confidence coefficients for confidence intervals on variance components," Communications in Statistics - Simulation and Computation, B19(4), 1165-1178. - Iyer, H. K. and Vecchia, D. F. (1990). "Minimum cost inspection intervals for a two-state process," Journal of Quality Technology, 22, 210-222. - Hwang, J. T. and Liu, H. K. (1990). "Sequential confidence regions in inverse regression problems," Annals of Statist. 18 1389-1399. - Vecchia, D. F., Iyer, H. K. and Chapman, P. L. (1989). "Calibration with randomly changing standard curves," Technometrics, 31, 83-90. - Vecchia, D. F. and Iyer, H. K. (1989). "Exact distribution-free tests for equality of several linear models," Communications in Statistics-Theory and Methods, 18, 2467-2488. - Iyer, H. K. and Vecchia, D. F. (1989). "Exact moments of the symmetric cubic assignment statistic," Communications in Statistics-Theory and Methods, 18, 4309-4320. - Hagwood, C. (1989). "A Renewal Theorem in Multidimensional Time," Australian Journal of Statistics, 31, 130-137. - Mee, R. W., Eberhardt, Keith R. and Reeve, C. P. (1989). Computing Factors for Exact Two-Sided Tolerance Limits for a Normal Distribution," Communications in Statistics-Simulation and Computation,18. - Eberhardt, Keith R., Reeve, C. P. and Spiegelman, C. H. (1989). "A Minimax Approach to Combining Means, With Practical Examples," Chemometrics and Intelligent Laboratory Systems. - Croarkin, Mary C. (1989). "An Extended Error Model for Comparison Calibration", Metrologia 26, 107-113. - Liggett, W. S. (1989), "Estimation of an Asymmetrical Density from Several Small Samples," Biometrika, 76, 13-21. - Wang, C. M. (1988). "One-sided confidence intervals for the positive linear combination of two variances," Communications in Statistics - Simulation and Computation, B17(1), 283-292. - Wang, C. M. (1988), "Beta-expectation tolerance limits for balanced one-way random-effects model," in Probability and Statistics: Essays in Honour of Franklin A. Graybill, J. N. Srivastava Ed., p. 285, Amsterdam: North Holland. - Yao, Y.C., Vecchia, D. F., and Iyer, H. K. (1988). "Linear calibration when the coefficient of variation is constant," Essays in Honor of F. A. Graybill, (Monograph), (J. N. Srivastava, Ed.), North-Holland, 297-309. - Mulrow, J. M., Vecchia, D. F., Buonaccorsi, J. P. and Iyer, H. K. (1988). "Problems with interval estimation when data are adjusted via calibration," Journal of Quality Technology, 20, 233-247. - Liggett, W. S. (1988), "Estimation of the Error Probability Density from Replicate Measurements on Several Items," Biometrika, 75, 557-567.

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• Volume 27, Issue 4
• Using creative methods of engagement to facilitate the inclusion of children and young people with diverse needs in research
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• http://orcid.org/0000-0001-9104-1999 Alison Rodriguez 1 ,
• http://orcid.org/0000-0002-7933-5182 Michael J Tatterton 2 , 3 ,
• Joanna Smith 4 , 5
• 1 Health Care , University of Leeds School of Healthcare , Leeds , UK
• 2 School of Nursing and Healthcare Leadership , University of Bradford , Bradford , UK
• 3 Bluebell Wood Children's Hospice , North Anston , UK
• 4 Department of Nursing and Midwifery , Sheffield Hallam University , Sheffield , UK
• 5 Sheffield Children's Hospital NHS Foundation Trust , Sheffield , UK
• Correspondence to Dr Alison Rodriguez; a.m.rodriguez{at}leeds.ac.uk

https://doi.org/10.1136/ebnurs-2024-104161

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• Nursing Research
• Child Health

There is a growing recognition of the need to include children and young people (CYP) in health research. Increasingly, funding bodies emphasise early engagement with those with lived experience, and to recruit participants who represent the diversity of the remit of the study. People with life-limiting conditions are typically under-represented in research because of their perceived vulnerability and as such, key voices are not represented in the research on which practice is based. While effective recruitment strategies can begin to address the breadth of CYP participating in research, data collection methods must meet their diverse needs, experiences, ages, stages of development and values to maximise the likelihood of engagement and involvement. We will outline participatory research methods aimed at facilitating CYP contribution to studies, drawing on our research and experiences of working with CYP with life-limiting conditions.

Engaging CYP with life-limiting conditions in research

Together for Short Lives and the Association for Paediatric Palliative Medicine established a joint research group to promote evidence-based practice for CYP with life-limiting conditions. 1 The group promotes how ethics committees and editorial boards should consider the approaches researchers take to undertaking research and the methods they employ with this group. It is a moral and ethical imperative that CYP are enabled to participate in research, and that the findings and their implementation represent their unique needs.

What research methods can be considered when collecting data from CYP?

• View inline

Advantages and disadvantages of participatory data collection methods

These ‘enabling methods’ can be valuable, supporting the sharing of rich experiences. They allow those who find it hard to express themselves to reveal more detailed and emotional stories than would be captured using traditional methods. 4

What have we learnt from undertaking research with CYP in palliative care contexts?

Although more studies are emerging that engage with CYP, there are still many studies within the palliative care field relying on proxy involvement of professionals and parents or other carers to represent the views of CYP with life-limiting conditions. Indeed, the voice of CYP with neurodegenerative conditions, who could offer insight into their experiences, in research is lacking. 5 In our own work, early engagement with CYP in the research design process enabled trust with families and CYP to feel comfortable in sharing their perspectives. Building rapport took time, commitment and engagement in activities outside of the study itself, to share information and explain our perspectives, in order for CYP to feel able to be involved and share their views. We benefited from the support of a young person with life-limiting conditions as a research team member and from an advisory group of young people, their siblings and parents. These foundations have supported the development of our studies, demonstrating our commitment to research that is of real-world relevance and importance to the CYP target population. 5 6 We spent time undertaking consultation activities and delivering workshops to explore research foci and how various methods of engagement could be used. 6 7 The workshops and activities undertaken used a range of data collection methods that have included digital Trello boards, closed Facebook group, X (formerly Twitter), Zoom and Teams discussion/activities.

Participatory methods can be used to scaffold or enhance results gained from more formal methods. Making data collection relevant to the developmental stage of the CYP, their personality and preferences is important. Not all CYP enjoy art, or have the confidence to join discussion groups or the latest technology or unlimited access to Wi-Fi, and methods must be relevant to the developmental stage and ability of CYP. Therefore, a mosaic approach, using a range of data collection strategies, can allow researchers the flexibility to meet the needs and preferences of participants. For example, children will often prefer to communicate with a range of senses, whereas young people may prefer to engage with more formal interview techniques or photographs where they can have some degree of autonomy. Measures validated for the population/age group are important but asking CYP questions about the suitability and burden of completing such activities and if the mode of delivery is suitable will help researchers to shape future studies. Participatory methods can also be used to scaffold or build on the results gained from more formal methods.

We have outlined some research methods that can be used with CYP. For these methods to be successful, building rapport and trust with participants prior to data collection is needed. It is important that researchers consider not only CYP participation in research, but supporting CYP to engage with, shape and understand studies that affect them. The use of creative activities alongside more traditional research methods can reduce barriers, enable CYP to feel comfortable and encourage their participation. Future research exploring the mechanisms and outcomes of CYP research incorporating creative participatory methods in different healthcare contexts would prove insightful for researchers considering research designs and particularly their methods of meaningful engagement.

Ethics approval

Not applicable.

• Bluebond-Langner M ,
• Chambers L ,
• Lapwood S , et al
• Haijes HA ,
• van Thiel G
• Montreuil M ,
• Bogossian A ,
• Laberge-Perrault E , et al
• Rodriguez AM ,
• Kellehear A ,
• Lanfranchi V , et al
• McSherry W , et al
• Rodriguez A ,
• Tatterton M , et al

X @ARodriguez339, @MJTatterton, @josmith175

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Provenance and peer review Not commissioned; internally peer reviewed.

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Promoting reusable and open methods and protocols (PRO-MaP) can improve methodological reporting in the life sciences

Roles Writing – review & editing

Current address: European Food Safety Agency, Parma, Italy

Affiliation European Commission, Joint Research Centre (JRC), Ispra, Italy

Current address: UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom

Affiliation NC3Rs, London, United Kingdom

Affiliation InterChange Research, Bloomsbury, London, United Kingdom

Affiliation eLife, Cambridge, United Kingdom

Affiliation European Commission, DG Research and Innovation (DG-RTD), Brussels, Belgium

Affiliation Nature Protocols, Springer Nature, Berlin, Germany

Affiliation Cell Press, Milan, Italy

Affiliation protocols.io, Springer Nature, Perth, United Kingdom

Affiliation Bio-protocol, Sunnyvale, California, United States of America

Affiliation PLOS, San Francisco, California, United States of America

Affiliation Science Europe, Brussels, Belgium

Affiliation Department of Biomedical Science, University Lausanne, Lausanne, Switzerland

Affiliation EMBO Press, Heidelberg, Germany

Affiliation F1000 Research, London, United Kingdom

Affiliations Bio-protocol, Sunnyvale, California, United States of America, Harvard Medical School, Boston, Massachusetts, United States of America

Affiliations Bio-protocol, Sunnyvale, California, United States of America, Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America

Affiliation FRESCI by Science&Strategy SL, Barcelona, Spain

•  [ ... ],

Roles Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Berlin Institute of Health at Charité – Universitätsmedizin Berlin, QUEST Center for Responsible Research, Berlin, Germany

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• Sofia Batista Leite, 
• Matthew A. Brooke, 
• Annamaria Carusi, 
• Andy Collings, 
• Pierre Deceuninck, 
• Jean-François Dechamp, 
• Bronwen Dekker, 
• Elisa De Ranieri, 
• Emma Ganley, 

Published: September 19, 2024

• https://doi.org/10.1371/journal.pbio.3002835
• Reader Comments

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Detailed method descriptions are essential for reproducibility, research evaluation, and effective data reuse. We summarize the key recommendations for life sciences researchers and research institutions described in the European Commission PRO-MaP report.

Citation: Batista Leite S, Brooke MA, Carusi A, Collings A, Deceuninck P, Dechamp J-F, et al. (2024) Promoting reusable and open methods and protocols (PRO-MaP) can improve methodological reporting in the life sciences. PLoS Biol 22(9): e3002835. https://doi.org/10.1371/journal.pbio.3002835

Copyright: © 2024 Batista Leite et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The author(s) received no specific funding for this work.

Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: M.L. is a current employee of PLOS. E.G. is a former Editor in Chief of PLOS Biology. V.S. was the founding Executive Director of PLOS and one of the launch editors for PLOS Biology. A.C. is a former PLOS employee. As is noted in the author affiliations, some of the experts who contributed to these guidelines are employed by protocol repositories, protocol journals or publishers that publish protocol papers, methods papers, protocol journals or methods journals. S.B.L. is employed with the European Food Safety Authority (EFSA) in the Unit PREV that provides scientific and administrative support to the Panel on Plant Protection Products and their Residues in the area Assess Dept. However, the present article is published under the sole responsibility of the author/s and may not be considered as an EFSA scientific output. The positions and opinions presented in this article are those of the author/s alone and do not represent the views/any official position or scientific works of EFSA. To know about the views or scientific outputs of EFSA, please consult its website under http://www.efsa.europa.eu . M.B. was an employee of the NC3R. His role included promoting the ARRIVE guidelines and coordinating the RIVER recommendations working group.

The open science movement has heavily focused on Open Access publications, and open and FAIR (Findable, Accessible, Interoperable, Reusable) data and code; however, the development of open methods has received comparatively little attention. An active community is needed to advance open, reusable methods and step-by-step protocols, as these research outputs are crucial for several reasons. First, reproducibility starts with methods; researchers cannot reproduce research findings or determine whether they are trustworthy without knowing how the data were generated [ 1 ]. Second, detailed methods are also crucial for responsible and effective data reuse, as they allow prospective data users to determine whether existing data sets are appropriate to answer new research questions, and whether the data were collected using a rigorous design that is likely to yield trustworthy and reproducible results. Third, in many fields, methods may be one of the most useful and reusable outputs that researchers create. Sharing methods, and giving credit to method developers, may accelerate scientific advancement. Unfortunately, the methods section of many research articles remains insufficient to reproduce results or reuse methods [ 2 , 3 ].

The Promoting Reusable and Open Methods and Protocols (PRO-MaP) recommendations [ 4 ] seek to address this problem by improving the reporting of detailed, reusable, and open methods and step-by-step protocols in the life sciences. PRO-MaP outlines actions that 4 stakeholder groups—researchers, research institutions and departments, publishers and editors, and funders—should take to achieve these goals. The recommendations focus on reusable step-by-step protocols, which describe how a specific procedure is performed, rather than study design protocols, which describe the research plan for a single study. PRO-MaP was conceptualized during a workshop convened by the EU Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM, https://joint-research-centre.ec.europa.eu/eu-reference-laboratory-alternatives-animal-testing-eurl-ecvam_en ) to bring together members of all stakeholder groups in June 2022. Recommendations were revised after receiving feedback from many members of each stakeholder group.

Readers can find complete, detailed recommendations for each stakeholder group in the full European Commission “Science for Policy” report [ 4 ], along with actions for implementing each recommendation. Here, we highlight important principles, and corresponding recommendations, for researchers and research institutions and departments ( Table 1 ).

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[PLEASE ENSURE THAT THE BOLDED TEXT IN THE PARAGRAPHS BELOW REMAINS IN BOLD FONT]

Share reusable step-by-step protocols and cite them in publications. Formulating methods as step-by-step procedures makes it easier to identify and supply information that is essential to implement and reproduce the work. This is of greater value than the free text descriptions used in many research papers, which only provide a general overview of the methods used. As described above, institutions and departments should incentivize and reward researchers for sharing detailed methods and reusable step-by-step protocols by including these outputs in research(er) evaluations. Research group leaders, institutions, and departments should also offer training, including in the use of study design and reporting guidelines (e.g., [ 5 – 7 ], https://www.equator-network.org ), using research resource identifiers (RRIDs) to unambiguously specify what reagents and organisms were used [ 8 ] ( https://scicrunch.org/resources ), techniques for writing reusable step-by-step protocols and depositing them in online repositories, and techniques for citing protocols in research papers.

Protocols should be citable and shared on dynamic platforms. While static method and protocol descriptions are essential for understanding (published) results, they reflect one researcher’s approach at a single time point and may quickly become outdated. Dynamic protocol sharing platforms, such as protocols.io [ 9 ], better capture the ever-changing reality of methods by allowing researchers to share updated versions of their protocol, or forks containing their adaptation of protocols from others. Each protocol object (with a DOI) represents the static protocol version used for a specific study; versioning and forking allows researchers to create new citable objects that more accurately reflect the methods used in their current experiments, while citing the static version of the protocol. Best practice is for researchers to both capture reusable step-by-step protocols detailing their research procedures and to use and maintain up-to-date versions of them. Research institutions and departments should provide training and reward researchers for publicly sharing and citing reusable protocols.

Use methodological shortcut citations responsibly. Researchers use a methodological shortcut citation when they cite another resource that used the method, instead of fully describing the method [ 10 ]. Shortcut citations are used to explain how something was done and may be accompanied by phrases like “briefly” or “as previously described.” However, shortcut citations can seriously impair understanding of the method if the resource cited is missing details needed to implement the method, also uses a shortcut citation, or is not accessible for everyone to read. When determining whether to cite a resource as a shortcut, researchers should confirm that the cited resource: (1) provides a detailed, reusable description; (2) describes the method used in the citing study; and (3) is Open Access [ 10 ]. Resources that don’t meet these criteria can be cited to give credit to the methods’ creators; however, should not be cited as shortcuts. Authors should either fully describe the method or find or create another resource that meets the criteria.

Facilitate cultural change. We need a cultural shift to reward and incentivize methods development and sharing of reusable, open methods and protocols. Rewarding open protocols, data and code alongside traditional publications is especially important; until this is achieved, researchers who share these valuable outputs will be doing more work without recognition. Research institutions and departments must incentivize cultural change by creating an environment that recognizes the value of sharing open and reproducible methods, and rewarding and incentivizing sharing of detailed methods and step-by-step protocols. Institutions, departments, and research group leaders can all disseminate the recommendations within their network. Institutions and departments may add a methods and protocols section to CV templates, include methods and protocol sharing in hiring, promotion and tenure evaluations, and make sharing and reporting of step-by-step protocols a thesis requirement.

While this article focuses on researchers and research institutions and departments, other stakeholders are also critical to incentivizing high-quality methods reporting. PRO-MaP highlights several actions that funders can take to reward and incentivize methods sharing, including providing resources to ensure that researchers have the capacity to do this additional work, recognizing methods and protocols as valued research outputs, and requiring sharing of methods and reusable step-by-step protocols from funded work. Publishers and editors can also facilitate, reward, and incentivize methods and protocol sharing when assessing and disseminating research papers. Ensuring that readers can find detailed methods that were used to generate data in published work is crucial to assess the quality of the work and to meet the research community’s needs.

The PRO-MaP authors welcome contributions and collaborations with stakeholders working to implement these recommendations. We seek to build a community where individuals and organizations can develop a shared multi-stakeholder action plan, learn from each other’s experiences, and work collaboratively to drive cultural change. We encourage researchers, as well as leadership and administrative staff in research institutions and departments, to work collaboratively to begin implementing the PRO-MaP recommendations. Individuals, research groups and organizations can start with 1 or 2 items that would be easy to implement, while exploring opportunities to implement more challenging items over time. Researchers and research institutions and departments can also encourage funders, publishers, and editors to implement the recommendations, as transformative change will require collaborative action across all stakeholder groups. We encourage everyone who is interested in working towards PRO-MaP implementation, exploring opportunities to adapt these recommendations to suit the needs of other research fields, and building a community to reward and incentivize sharing of open and reusable methods and protocols, to contact the report authors. The lack of detailed methods and protocols is a major impediment to reproducibility. We must work collaboratively to make research publications more reusable and reproducible.

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Estimate of How Often LGBTQ Youth Attempt Suicide in the U.S.

Suicide is the second leading cause of death among young people (Centers for Disease Control and Prevention, 2020), with LGBTQ youth being four times more likely to seriously consider suicide, to make a plan for suicide, and to attempt suicide than their peers (Johns et al., 2019; Johns et al., 2020). Understanding the number of LGBTQ youth who seriously consider and attempt suicide, as well as how often suicide risk occurs, improves our ability to serve and advocate for LGBTQ youth.

The Trevor Project estimates that at least one LGBTQ youth between the ages of 13–24 attempts suicide every 45 seconds in the U.S. 

Because many LGBTQ youth report attempting suicide multiple times in a given year, this estimate likely underrepresents the extent of how often LGBTQ youth attempt suicide in the U.S. Additionally, The Trevor Project’s past-year attempted suicide rates are based on non-probability data that trend slightly slower than rates among national probability datasets

Methodology

A. estimating the number of lgbtq youth ages of 13–24 in the u.s. .

This estimate is based on Trevor’s previous estimates of the total number of LGBTQ youth between ages 13–24 who live in the U.S. each year. The process for deriving these numbers can be found in The Trevor Project’s  National Estimate of LGBTQ Youth Seriously Considering Suicide . Based on our previous estimation process, there are approximately 2,647,755 LGBTQ youth living in the United States who are between the ages of 13–18 and approximately 2,529,117 who are between the ages of 19–24.

B. Estimating the Number of LGBTQ Youth Who Attempted Suicide in the Past Year

The Trevor Project’s 2021 National Survey of LGBTQ Youth Mental Health found that 19.0% of LGBTQ youth ages 13–18 and 8.3% of LGBTQ youth ages 19–24 reported attempting suicide in the past year. Applying these rates to the estimates of LGBTQ youth living in the U.S. results in an estimated 503,073 LGBTQ youth between the ages of 13–18 and 209,917 between the ages of 19–24 who attempted suicide in the past year, for a total of 712,990 LGBTQ youth between the ages of 13–24.

C. Estimating How Often LGBTQ Youth Attempt Suicide in the U.S.

To approximate how often attempts occur, the estimated total number of LGBTQ youth ages 13–24 who attempted suicide (712,990) was divided by the total number of minutes in a year (525,600), resulting in an estimated 1.356525992 attempts each minute or .02260876654 per second. Dividing the number 1 by the estimate per second, provides an estimate of how often an attempt occurs in seconds, which was 44.23063055. As such, we estimate that at least one LGBTQ youth between the ages of 13–24 attempts suicide in the U.S. every 45 seconds.

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